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For: Schüssler-Fiorenza Rose SM, Contrepois K, Moneghetti KJ, Zhou W, Mishra T, Mataraso S, Dagan-Rosenfeld O, Ganz AB, Dunn J, Hornburg D, Rego S, Perelman D, Ahadi S, Sailani MR, Zhou Y, Leopold SR, Chen J, Ashland M, Christle JW, Avina M, Limcaoco P, Ruiz C, Tan M, Butte AJ, Weinstock GM, Slavich GM, Sodergren E, McLaughlin TL, Haddad F, Snyder MP. A longitudinal big data approach for precision health. Nat Med 2019;25:792-804. [PMID: 31068711 DOI: 10.1038/s41591-019-0414-6] [Cited by in Crossref: 211] [Cited by in F6Publishing: 218] [Article Influence: 70.3] [Reference Citation Analysis]
Number Citing Articles
1 Li P, Luo H, Ji B, Nielsen J. Machine learning for data integration in human gut microbiome. Microb Cell Fact 2022;21:241. [PMID: 36419034 DOI: 10.1186/s12934-022-01973-4] [Reference Citation Analysis]
2 Maitre L, Bustamante M, Hernández-ferrer C, Thiel D, Lau CE, Siskos AP, Vives-usano M, Ruiz-arenas C, Pelegrí-sisó D, Robinson O, Mason D, Wright J, Cadiou S, Slama R, Heude B, Casas M, Sunyer J, Papadopoulou EZ, Gutzkow KB, Andrusaityte S, Grazuleviciene R, Vafeiadi M, Chatzi L, Sakhi AK, Thomsen C, Tamayo I, Nieuwenhuijsen M, Urquiza J, Borràs E, Sabidó E, Quintela I, Carracedo Á, Estivill X, Coen M, González JR, Keun HC, Vrijheid M. Multi-omics signatures of the human early life exposome. Nat Commun 2022;13:7024. [DOI: 10.1038/s41467-022-34422-2] [Reference Citation Analysis]
3 Zaghlool SB, Halama A, Stephan N, Gudmundsdottir V, Gudnason V, Jennings LL, Thangam M, Ahlqvist E, Malik RA, Albagha OME, Abou‑samra AB, Suhre K. Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population. Nat Commun 2022;13:7121. [DOI: 10.1038/s41467-022-34754-z] [Reference Citation Analysis]
4 Carrasco-zanini J, Pietzner M, Lindbohm JV, Wheeler E, Oerton E, Kerrison N, Simpson M, Westacott M, Drolet D, Kivimaki M, Ostroff R, Williams SA, Wareham NJ, Langenberg C. Proteomic signatures for identification of impaired glucose tolerance. Nat Med 2022. [DOI: 10.1038/s41591-022-02055-z] [Reference Citation Analysis]
5 Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, Wang F, Cheng F, Luo Y. Multimodal machine learning in precision health: A scoping review. npj Digit Med 2022;5:171. [DOI: 10.1038/s41746-022-00712-8] [Reference Citation Analysis]
6 Cai X, Xue Z, Zeng F, Tang J, Yue L, Wang B, Ge W, Xie Y, Miao Z, Gou W, Fu Y, Li S, Gao J, Shuai M, Zhang K, Xu F, Tian Y, Xiang N, Zhou Y, Shan P, Zhu Y, Chen Y, Zheng J, Guo T. Population serum proteomics uncovers prognostic protein classifier and molecular mechanisms for metabolic syndrome.. [DOI: 10.1101/2022.10.21.22281353] [Reference Citation Analysis]
7 Panyard DJ, Yu B, Snyder MP. The metabolomics of human aging: Advances, challenges, and opportunities. Sci Adv 2022;8:eadd6155. [PMID: 36260671 DOI: 10.1126/sciadv.add6155] [Reference Citation Analysis]
8 Zhou Z, Huang C, Fu P, Huang H, Zhang Q, Wu X, Yu Q, Sun Y. Prediction of in-hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury. CNS Neurosci Ther 2022. [PMID: 36258296 DOI: 10.1111/cns.13993] [Reference Citation Analysis]
9 Chakaroun RM, Olsson LM, Bäckhed F. The potential of tailoring the gut microbiome to prevent and treat cardiometabolic disease. Nat Rev Cardiol 2022. [PMID: 36241728 DOI: 10.1038/s41569-022-00771-0] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
10 Spencer EA, Agrawal M, Jess T. Prognostication in inflammatory bowel disease. Front Med (Lausanne) 2022;9:1025375. [PMID: 36275829 DOI: 10.3389/fmed.2022.1025375] [Reference Citation Analysis]
11 Fang X, Miao R, Wei J, Wu H, Tian J. Advances in multi-omics study of biomarkers of glycolipid metabolism disorder. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.10.030] [Reference Citation Analysis]
12 Xu J, Lan Y, Wang X, Shang K, Liu X, Wang J, Li J, Yue B, Shao M, Fan Z. Multi-omics analysis reveals the host–microbe interactions in aged rhesus macaques. Front Microbiol 2022;13:993879. [DOI: 10.3389/fmicb.2022.993879] [Reference Citation Analysis]
13 Buergel T, Steinfeldt J, Ruyoga G, Pietzner M, Bizzarri D, Vojinovic D, Upmeier Zu Belzen J, Loock L, Kittner P, Christmann L, Hollmann N, Strangalies H, Braunger JM, Wild B, Chiesa ST, Spranger J, Klostermann F, van den Akker EB, Trompet S, Mooijaart SP, Sattar N, Jukema JW, Lavrijssen B, Kavousi M, Ghanbari M, Ikram MA, Slagboom E, Kivimaki M, Langenberg C, Deanfield J, Eils R, Landmesser U. Metabolomic profiles predict individual multidisease outcomes. Nat Med 2022. [PMID: 36138150 DOI: 10.1038/s41591-022-01980-3] [Reference Citation Analysis]
14 Papazian S, Fornaroli C, Bonnefille B, Pesquet E, Xie H, Martin JW. Silicone Foam for Passive Sampling and Nontarget Analysis of Air. Environ Sci Technol Lett . [DOI: 10.1021/acs.estlett.2c00489] [Reference Citation Analysis]
15 Xiao T, Dong X, Lu Y, Zhou W. High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population. Phenomics 2022. [DOI: 10.1007/s43657-022-00071-0] [Reference Citation Analysis]
16 Güntner AT, Weber IC, Schon S, Pratsinis SE, Gerber PA. Monitoring rapid metabolic changes in health and type-1 diabetes with breath acetone sensors. Sensors and Actuators B: Chemical 2022;367:132182. [DOI: 10.1016/j.snb.2022.132182] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
17 Goodrich JM, Tang L, Carmona YR, Meijer JL, Perng W, Watkins DJ, Meeker JD, Mercado-garcía A, Cantoral A, Song PX, Téllez-rojo MM, Peterson KE. Trimester-specific phthalate exposures in pregnancy are associated with circulating metabolites in children. PLoS ONE 2022;17:e0272794. [DOI: 10.1371/journal.pone.0272794] [Reference Citation Analysis]
18 Bahmani A. Deep Data and Precision Health. Inside Precision Medicine 2022;9:44-46. [DOI: 10.1089/ipm.09.04.12] [Reference Citation Analysis]
19 Zheng M, Piermarocchi C, Mias GI. Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects. Sci Rep 2022;12:12098. [PMID: 35840765 DOI: 10.1038/s41598-022-16326-9] [Reference Citation Analysis]
20 Mor U, Cohen Y, Valdés-mas R, Kviatcovsky D, Elinav E, Avron H. Dimensionality reduction of longitudinal ’omics data using modern tensor factorizations. PLoS Comput Biol 2022;18:e1010212. [DOI: 10.1371/journal.pcbi.1010212] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Gupta S, Sing JC, Röst HL. Signal Alignment Enables Analysis of DIA Proteomics Data from Multisite Experiments.. [DOI: 10.1101/2022.07.10.498897] [Reference Citation Analysis]
22 Goossens E, Dehau T, Ducatelle R, Van Immerseel F. Omics technologies in poultry health and productivity - part 2: future applications in the poultry industry. Avian Pathol 2022;:1-6. [PMID: 35675218 DOI: 10.1080/03079457.2022.2085545] [Reference Citation Analysis]
23 Sasaki E, Yamamoto H, Asari T, Matsuta R, Ota S, Kimura Y, Sasaki S, Ishibashi K, Yamamoto Y, Kami K, Ando M, Tsuda E, Ishibashi Y. Metabolomics with severity of radiographic knee osteoarthritis and early phase synovitis in middle-aged women from the Iwaki Health Promotion Project: a cross-sectional study. Arthritis Res Ther 2022;24:145. [PMID: 35710532 DOI: 10.1186/s13075-022-02830-w] [Reference Citation Analysis]
24 Liu F, Pan W. Risk Prediction of E-Payment by Big Data Management Technology. Mathematical Problems in Engineering 2022;2022:1-8. [DOI: 10.1155/2022/6815255] [Reference Citation Analysis]
25 Picard M. Why Do We Care More About Disease than Health? Phenomics 2022;2:145-55. [DOI: 10.1007/s43657-021-00037-8] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Büchner S, Marschollek M, Foadi N. Komplexitätssteigerung medizinischer Entscheidungssituationen – Herausforderungen der Digitalisierung erkennen und gestalten. Gesundheitsökonomie & Qualitätsmanagement 2022;27:138-143. [DOI: 10.1055/a-1695-4507] [Reference Citation Analysis]
27 Swanson T, Zelner J, Guikema S. COVID-19 has illuminated the need for clearer AI-based risk management strategies. Journal of Risk Research. [DOI: 10.1080/13669877.2022.2077411] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Merino J. Precision nutrition in diabetes: when population-based dietary advice gets personal. Diabetologia 2022. [PMID: 35593923 DOI: 10.1007/s00125-022-05721-6] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
29 Fakouri Baygi S, Kumar Y, Barupal DK. IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets. J Proteome Res 2022. [PMID: 35579321 DOI: 10.1021/acs.jproteome.2c00120] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
30 Song H, Ma Y, Chen H, Algalil FA. Health Promotion Effects of Sports Training Based on HMM Theory and Big Data. Applied Bionics and Biomechanics 2022;2022:1-10. [DOI: 10.1155/2022/6110247] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Rhodes CJ, Sweatt AJ, Maron BA. Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension. Circ Res 2022;130:1423-44. [PMID: 35482840 DOI: 10.1161/CIRCRESAHA.121.319969] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Jifang Y, Jianguo C, Lakshmanna K. Construction of Intelligent Service System for Adolescent Students’ PE Based on Big Data Analysis. Wireless Communications and Mobile Computing 2022;2022:1-8. [DOI: 10.1155/2022/4922918] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Lang C. Annual Dynamics of Blood Lipid Parameters in Highly Qualified Physical Training. Appl Biochem Biotechnol 2022. [PMID: 35451795 DOI: 10.1007/s12010-022-03918-4] [Reference Citation Analysis]
34 Shei RJ, Holder IG, Oumsang AS, Paris BA, Paris HL. Wearable activity trackers-advanced technology or advanced marketing? Eur J Appl Physiol 2022. [PMID: 35445837 DOI: 10.1007/s00421-022-04951-1] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 Heath L, Earls JC, Magis AT, Kornilov SA, Lovejoy JC, Funk CC, Rappaport N, Logsdon BA, Mangravite LM, Kunkle BW, Martin ER, Naj AC, Ertekin-Taner N, Golde TE, Hood L, Price ND; Alzheimer’s Disease Genetics Consortium. Manifestations of Alzheimer's disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90. Sci Rep 2022;12:6117. [PMID: 35413975 DOI: 10.1038/s41598-022-09825-2] [Reference Citation Analysis]
36 Powell J, Li X. Integrated, data-driven health management: A step closer to personalized and predictive healthcare. Cell Syst 2022;13:201-3. [PMID: 35298911 DOI: 10.1016/j.cels.2022.02.001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Apaolaza I, San José-enériz E, Valcarcel LV, Agirre X, Prosper F, Planes FJ. A network-based approach to integrate nutrient microenvironment in the prediction of synthetic lethality in cancer metabolism. PLoS Comput Biol 2022;18:e1009395. [DOI: 10.1371/journal.pcbi.1009395] [Reference Citation Analysis]
38 Chelliah R, Banan-MwineDaliri E, Khan I, Wei S, Elahi F, Yeon SJ, Selvakumar V, Ofosu FK, Rubab M, Ju HH, Rallabandi HR, Madar IH, Sultan G, Oh DH. A review on the application of bioinformatics tools in food microbiome studies. Brief Bioinform 2022:bbac007. [PMID: 35189636 DOI: 10.1093/bib/bbac007] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
39 Aasmets O, Krigul KL, Lüll K, Metspalu A, Org E. Gut metagenome associations with extensive digital health data in a volunteer-based Estonian microbiome cohort. Nat Commun 2022;13:869. [PMID: 35169130 DOI: 10.1038/s41467-022-28464-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
40 Leopold JA. Personalizing treatments for patients based on cardiovascular phenotyping. Expert Review of Precision Medicine and Drug Development. [DOI: 10.1080/23808993.2022.2028548] [Reference Citation Analysis]
41 Zaghlool SB, Halama A, Stephan N, Thangam M, Ahlqvist E, Albagha OME, Abou⍰samra AB, Suhre K. Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population.. [DOI: 10.1101/2022.01.13.22269204] [Reference Citation Analysis]
42 Velten B, Braunger JM, Argelaguet R, Arnol D, Wirbel J, Bredikhin D, Zeller G, Stegle O. Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO. Nat Methods 2022. [PMID: 35027765 DOI: 10.1038/s41592-021-01343-9] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 15.0] [Reference Citation Analysis]
43 Herder C, Roden M. A novel diabetes typology: towards precision diabetology from pathogenesis to treatment. Diabetologia 2022. [PMID: 34981134 DOI: 10.1007/s00125-021-05625-x] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 8.0] [Reference Citation Analysis]
44 Wilson S, Steele S, Adeli K. Innovative technological advancements in laboratory medicine: Predicting the lab of the future. Biotechnology & Biotechnological Equipment 2022;36:S9-S21. [DOI: 10.1080/13102818.2021.2011413] [Reference Citation Analysis]
45 Jia H. Blurring the line between opportunistic pathogens and commensals. Investigating Human Diseases with the Microbiome 2022. [DOI: 10.1016/b978-0-323-91369-0.00007-8] [Reference Citation Analysis]
46 Boffetta P, Collatuzzo G. Application of P4 (Predictive, Preventive, Personalized, Participatory) Approach to Occupational Medicine. Med Lav 2022;113:e2022009. [PMID: 35226650 DOI: 10.23749/mdl.v113i1.12622] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
47 Piening BD, Dowdell AK, Snyder MP. Elucidating Diversity in Obesity-Related Phenotypes Using Longitudinal and Multi-omic Approaches. Natural Products in Obesity and Diabetes 2022. [DOI: 10.1007/978-3-030-92196-5_2] [Reference Citation Analysis]
48 Dikarlo P, Dorst I, Moskalenko O, Yateem M. Precision Nutrition from the View of the Gut Microbiome. Advances in Precision Nutrition, Personalization and Healthy Aging 2022. [DOI: 10.1007/978-3-031-10153-3_4] [Reference Citation Analysis]
49 Ma KS. Screening programs incorporating big data analytics. Big Data Analytics for Healthcare 2022. [DOI: 10.1016/b978-0-323-91907-4.00023-6] [Cited by in Crossref: 4] [Article Influence: 4.0] [Reference Citation Analysis]
50 Graydon C, Teede H, Sullivan C, De Silva K, Enticott J. Driving impact through big data utilization and analytics in the context of a Learning Health System. Big Data Analytics for Healthcare 2022. [DOI: 10.1016/b978-0-323-91907-4.00019-4] [Reference Citation Analysis]
51 Chin A, Rider NL. Artificial Intelligence in Clinical Immunology. Artificial Intelligence in Medicine 2022. [DOI: 10.1007/978-3-030-64573-1_83] [Reference Citation Analysis]
52 Valadez-barba V, Cota-coronado A, Barragán-álvarez C, Padilla-camberos E, Díaz-martínez N. iPSC for modeling of metabolic and neurodegenerative disorders. Novel Concepts in iPSC Disease Modeling 2022. [DOI: 10.1016/b978-0-12-823882-0.00007-2] [Reference Citation Analysis]
53 Hauser AS. Personalized Medicine Through GPCR Pharmacogenomics. Comprehensive Pharmacology 2022. [DOI: 10.1016/b978-0-12-820472-6.00100-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
54 He H, Pan L, Hu Y, Tu J, Yu C, Shan G. Peking Union Health Application (PUHApp): Modern solution for effective epidemiological survey administration and health promotion. Global Transitions 2022;4:40-44. [DOI: 10.1016/j.glt.2022.10.003] [Reference Citation Analysis]
55 Ben-yacov O, Rein M. Precision Nutrition for Type 2 Diabetes. Precision Medicine in Diabetes 2022. [DOI: 10.1007/978-3-030-98927-9_12] [Reference Citation Analysis]
56 Zheng M, Piermarocchi C, Mias GI. Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects.. [DOI: 10.1101/2021.12.08.471816] [Reference Citation Analysis]
57 Verdonk F, Einhaus J, Tsai AS, Hedou J, Choisy B, Gaudilliere D, Kin C, Aghaeepour N, Angst MS, Gaudilliere B. Measuring the human immune response to surgery: multiomics for the prediction of postoperative outcomes. Curr Opin Crit Care 2021;27:717-25. [PMID: 34545029 DOI: 10.1097/MCC.0000000000000883] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
58 Marabita F, James T, Karhu A, Virtanen H, Kettunen K, Stenlund H, Boulund F, Hellström C, Neiman M, Mills R, Perheentupa T, Laivuori H, Helkkula P, Byrne M, Jokinen I, Honko H, Kallonen A, Ermes M, Similä H, Lindholm M, Widén E, Ripatti S, Perälä-Heape M, Engstrand L, Nilsson P, Moritz T, Miettinen T, Sallinen R, Kallioniemi O. Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health. Cell Syst 2021:S2405-4712(21)00451-8. [PMID: 34856119 DOI: 10.1016/j.cels.2021.11.001] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
59 Ward RA, Aghaeepour N, Bhattacharyya RP, Clish CB, Gaudillière B, Hacohen N, Mansour MK, Mudd PA, Pasupneti S, Presti RM, Rhee EP, Sen P, Spec A, Tam JM, Villani AC, Woolley AE, Hsu JL, Vyas JM. Harnessing the Potential of Multiomics Studies for Precision Medicine in Infectious Disease. Open Forum Infect Dis 2021;8:ofab483. [PMID: 34805429 DOI: 10.1093/ofid/ofab483] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
60 Cefalu WT, Andersen DK, Arreaza-Rubín G, Pin CL, Sato S, Verchere CB, Woo M, Rosenblum ND; symposium planning committee, moderators, and speakers. Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases. Can J Diabetes 2021:S1499-2671(21)00396-8. [PMID: 34794897 DOI: 10.1016/j.jcjd.2021.09.126] [Reference Citation Analysis]
61 Viana JN, Edney S, Gondalia S, Mauch C, Sellak H, O'Callaghan N, Ryan JC. Trends and gaps in precision health research: a scoping review. BMJ Open 2021;11:e056938. [PMID: 34697128 DOI: 10.1136/bmjopen-2021-056938] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
62 Yamada S, Bartunek J, Behfar A, Terzic A. Mass Customized Outlook for Regenerative Heart Failure Care. Int J Mol Sci 2021;22:11394. [PMID: 34768825 DOI: 10.3390/ijms222111394] [Reference Citation Analysis]
63 Balters S, Gowda N, Ordonez F, Paredes PE. Individualized stress detection using an unmodified car steering wheel. Sci Rep 2021;11:20646. [PMID: 34667184 DOI: 10.1038/s41598-021-00062-7] [Reference Citation Analysis]
64 Ahmed Z. Intelligent health system for the investigation of consenting COVID-19 patients and precision medicine. Per Med 2021;18:573-82. [PMID: 34619976 DOI: 10.2217/pme-2021-0068] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
65 Morrow JD, Castaldi PJ, Chase RP, Yun JH, Lee S, Liu YY, Hersh CP. Peripheral blood microbial signatures in current and former smokers. Sci Rep 2021;11:19875. [PMID: 34615932 DOI: 10.1038/s41598-021-99238-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
66 Sichko S, Bui TQ, Vinograd M, Shields GS, Saha K, Devkota S, Olvera-Alvarez HA, Carroll JE, Cole SW, Irwin MR, Slavich GM. Psychobiology of Stress and Adolescent Depression (PSY SAD) Study: Protocol overview for an fMRI-based multi-method investigation. Brain Behav Immun Health 2021;17:100334. [PMID: 34595481 DOI: 10.1016/j.bbih.2021.100334] [Reference Citation Analysis]
67 Bahmani A, Alavi A, Buergel T, Upadhyayula S, Wang Q, Ananthakrishnan SK, Alavi A, Celis D, Gillespie D, Young G, Xing Z, Nguyen MHH, Haque A, Mathur A, Payne J, Mazaheri G, Li JK, Kotipalli P, Liao L, Bhasin R, Cha K, Rolnik B, Celli A, Dagan-Rosenfeld O, Higgs E, Zhou W, Berry CL, Van Winkle KG, Contrepois K, Ray U, Bettinger K, Datta S, Li X, Snyder MP. A scalable, secure, and interoperable platform for deep data-driven health management. Nat Commun 2021;12:5757. [PMID: 34599181 DOI: 10.1038/s41467-021-26040-1] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
68 Misheva M, Ilott NE, Mccullagh JS. Recent advances and future directions in microbiome metabolomics. Current Opinion in Endocrine and Metabolic Research 2021;20:100283. [DOI: 10.1016/j.coemr.2021.07.001] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
69 Higgs E, Dagan-Rosenfeld O, Snyder M. Adapting skills from genetic counseling to wearables technology research during the COVID-19 pandemic: Poised for the pivot. J Genet Couns 2021;30:1269-75. [PMID: 34580951 DOI: 10.1002/jgc4.1509] [Reference Citation Analysis]
70 Wang C, Hu J, Blaser MJ, Li H. Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome study. BMC Genomics 2021;22:667. [PMID: 34525957 DOI: 10.1186/s12864-021-07948-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
71 Apaolaza I, José-eneriz ES, Valcarcel LV, Agirre X, Prósper F, Planes FJ. A network-based approach to integrate nutrient environment in the prediction of synthetic lethality in cancer metabolism.. [DOI: 10.1101/2021.09.01.458495] [Reference Citation Analysis]
72 Clarke SL, Assimes TL, Tcheandjieu C. The Propagation of Racial Disparities in Cardiovascular Genomics Research. Circ Genom Precis Med 2021;14:e003178. [PMID: 34461749 DOI: 10.1161/CIRCGEN.121.003178] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
73 Raijada D, Wac K, Greisen E, Rantanen J, Genina N. Integration of personalized drug delivery systems into digital health. Adv Drug Deliv Rev 2021;176:113857. [PMID: 34389172 DOI: 10.1016/j.addr.2021.113857] [Cited by in Crossref: 18] [Cited by in F6Publishing: 18] [Article Influence: 18.0] [Reference Citation Analysis]
74 Liu C, Sun YV. Anticipation of Precision Diabetes and Promise of Integrative Multi-Omics. Endocrinol Metab Clin North Am 2021;50:559-74. [PMID: 34399961 DOI: 10.1016/j.ecl.2021.05.011] [Reference Citation Analysis]
75 Cui M. Big data medical behavior analysis based on machine learning and wireless sensors. Neural Comput & Applic 2022;34:9413-27. [DOI: 10.1007/s00521-021-06369-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
76 Butte KD, Bahmani A, Butte AJ, Li X, Snyder MP. Five-year pediatric use of a digital wearable fitness device: lessons from a pilot case study. JAMIA Open 2021;4:ooab054. [PMID: 34350390 DOI: 10.1093/jamiaopen/ooab054] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
77 Kelly JR, Minuto C, Cryan JF, Clarke G, Dinan TG. The role of the gut microbiome in the development of schizophrenia. Schizophrenia Research 2021;234:4-23. [DOI: 10.1016/j.schres.2020.02.010] [Cited by in Crossref: 27] [Cited by in F6Publishing: 32] [Article Influence: 27.0] [Reference Citation Analysis]
78 Koppad S, B A, Gkoutos GV, Acharjee A. Cloud Computing Enabled Big Multi-Omics Data Analytics. Bioinform Biol Insights 2021;15:11779322211035921. [PMID: 34376975 DOI: 10.1177/11779322211035921] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
79 Demertzis K, Taketzis D, Tsiotas D, Magafas L, Iliadis L, Kikiras P. Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis. Processes 2021;9:1267. [DOI: 10.3390/pr9081267] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
80 O'donoghue SI. Grand Challenges in Bioinformatics Data Visualization. Front Bioinform 2021;1:669186. [DOI: 10.3389/fbinf.2021.669186] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
81 Shen SY, Žurauskienė J, Wei DM, Chen NN, Lu JH, Kuang YS, Liu HH, Cazier JB, Qiu X. Identification of maternal continuous glucose monitoring metrics related to newborn birth weight in pregnant women with gestational diabetes. Endocrine 2021. [PMID: 34125410 DOI: 10.1007/s12020-021-02787-x] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
82 Potter T, Vieira R, de Roos B. Perspective: Application of N-of-1 Methods in Personalized Nutrition Research. Adv Nutr 2021;12:579-89. [PMID: 33460438 DOI: 10.1093/advances/nmaa173] [Cited by in Crossref: 12] [Cited by in F6Publishing: 14] [Article Influence: 12.0] [Reference Citation Analysis]
83 Hussain MS, Silvera-Tawil D, Farr-Wharton G. Technology assessment framework for precision health applications. Int J Technol Assess Health Care 2021;37:e67. [PMID: 34034854 DOI: 10.1017/S0266462321000350] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
84 Dunn J, Kidzinski L, Runge R, Witt D, Hicks JL, Schüssler-Fiorenza Rose SM, Li X, Bahmani A, Delp SL, Hastie T, Snyder MP. Wearable sensors enable personalized predictions of clinical laboratory measurements. Nat Med 2021;27:1105-12. [PMID: 34031607 DOI: 10.1038/s41591-021-01339-0] [Cited by in Crossref: 47] [Cited by in F6Publishing: 52] [Article Influence: 47.0] [Reference Citation Analysis]
85 Sun YV, Liu C, Staimez L, Ali MK, Chang H, Kondal D, Patel S, Jones D, Mohan V, Tandon N, Prabhakaran D, Quyyumi AA, Narayan KMV, Agrawal A. Cardiovascular disease risk and pathophysiology in South Asians: can longitudinal multi-omics shed light? Wellcome Open Res 2020;5:255. [PMID: 34136649 DOI: 10.12688/wellcomeopenres.16336.2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
86 David A, Chaker J, Price EJ, Bessonneau V, Chetwynd AJ, Vitale CM, Klánová J, Walker DI, Antignac JP, Barouki R, Miller GW. Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives. Environ Int 2021;156:106630. [PMID: 34004450 DOI: 10.1016/j.envint.2021.106630] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 15.0] [Reference Citation Analysis]
87 Dong X, Liu C, Dozmorov M. Review of multi-omics data resources and integrative analysis for human brain disorders. Brief Funct Genomics 2021;20:223-34. [PMID: 33969380 DOI: 10.1093/bfgp/elab024] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
88 Maitre L, Bustamante M, Hernández-ferrer C, Thiel D, Lau C, Siskos A, Vives-usano M, Ruiz-arenas C, Robinson O, Mason D, Wright J, Cadiou S, Slama R, Heude B, Gallego-paüls M, Casas M, Sunyer J, Papadopoulou EZ, Gutzkow KB, Andrusaityte S, Grazuleviciene R, Vafeiadi M, Chatzi L, Sakhi AK, Thomsen C, Tamayo I, Nieuwenhuijsen M, Urquiza J, Borràs E, Sabidó E, Quintela I, Carracedo Á, Estivill X, Coen M, González JR, Keun HC, Vrijheid M. Multi-omics signatures of the human early life exposome.. [DOI: 10.1101/2021.05.04.21256605] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
89 Gao P, Shen X, Zhang X, Jiang C, Zhang S, Zhou X, Schüssler-fiorenza Rose SM, Snyder M. Precision environmental health monitoring by longitudinal exposome and multi-omics profiling.. [DOI: 10.1101/2021.05.05.442855] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
90 Duarte JD, Cavallari LH. Pharmacogenetics to guide cardiovascular drug therapy. Nat Rev Cardiol 2021;18:649-65. [PMID: 33953382 DOI: 10.1038/s41569-021-00549-w] [Cited by in Crossref: 10] [Cited by in F6Publishing: 12] [Article Influence: 10.0] [Reference Citation Analysis]
91 Gramegna LL, Evangelisti S, Di Vito L, La Morgia C, Maresca A, Caporali L, Amore G, Talozzi L, Bianchini C, Testa C, Manners DN, Cortesi I, Valentino ML, Liguori R, Carelli V, Tonon C, Lodi R. Brain MRS correlates with mitochondrial dysfunction biomarkers in MELAS-associated mtDNA mutations. Ann Clin Transl Neurol 2021;8:1200-11. [PMID: 33951347 DOI: 10.1002/acn3.51329] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
92 Zou J, Schiebinger L. Ensuring that biomedical AI benefits diverse populations. EBioMedicine 2021;67:103358. [PMID: 33962897 DOI: 10.1016/j.ebiom.2021.103358] [Cited by in Crossref: 13] [Cited by in F6Publishing: 15] [Article Influence: 13.0] [Reference Citation Analysis]
93 Wainberg M, Kamber RA, Balsubramani A, Meyers RM, Sinnott-Armstrong N, Hornburg D, Jiang L, Chan J, Jian R, Gu M, Shcherbina A, Dubreuil MM, Spees K, Meuleman W, Snyder MP, Bassik MC, Kundaje A. A genome-wide atlas of co-essential modules assigns function to uncharacterized genes. Nat Genet 2021;53:638-49. [PMID: 33859415 DOI: 10.1038/s41588-021-00840-z] [Cited by in Crossref: 40] [Cited by in F6Publishing: 45] [Article Influence: 40.0] [Reference Citation Analysis]
94 Banerjee A, Chen S, Fatemifar G, Zeina M, Lumbers RT, Mielke J, Gill S, Kotecha D, Freitag DF, Denaxas S, Hemingway H. Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility. BMC Med 2021;19:85. [PMID: 33820530 DOI: 10.1186/s12916-021-01940-7] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 15.0] [Reference Citation Analysis]
95 Tippairote T, Peana M, Chirumbolo S, Bjørklund G. Individual risk management strategy for SARS-CoV-2 infection: A step toward personalized healthcare. Int Immunopharmacol 2021;96:107629. [PMID: 33862554 DOI: 10.1016/j.intimp.2021.107629] [Reference Citation Analysis]
96 Heath L, Earls JC, Magis AT, Kornilov SA, Lovejoy JC, Funk CC, Rappaport N, Logsdon BA, Mangravite LM, Kunkle BW, Martin ER, Naj AC, Ertekin-taner N, Golde TE, Hood L, Price ND, Alzheimer’s Disease Genetics Consortium. Manifestations of genetic risk for Alzheimer’s Disease in the blood: a cross-sectional multi-omic analysis in healthy adults aged 18-90+.. [DOI: 10.1101/2021.03.26.437267] [Reference Citation Analysis]
97 Li Y, Ma L, Wu D, Chen G. Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine. Brief Bioinform 2021:bbab024. [PMID: 33778867 DOI: 10.1093/bib/bbab024] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 12.0] [Reference Citation Analysis]
98 Lam B, Catt M, Cassidy S, Bacardit J, Darke P, Butterfield S, Alshabrawy O, Trenell M, Missier P. Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning-Based Cross-sectional Study of the UK Biobank Accelerometer Cohort. JMIR Diabetes 2021;6:e23364. [PMID: 33739298 DOI: 10.2196/23364] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
99 Fawaz M. Role of nurses in precision health. Nurs Outlook 2021:S0029-6554(21)00020-8. [PMID: 33745686 DOI: 10.1016/j.outlook.2021.01.016] [Reference Citation Analysis]
100 Zheng M, Domanskyi S, Piermarocchi C, Mias GI. Visibility graph based temporal community detection with applications in biological time series. Sci Rep 2021;11:5623. [PMID: 33707481 DOI: 10.1038/s41598-021-84838-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
101 Pietzner M, Stewart ID, Raffler J, Khaw KT, Michelotti GA, Kastenmüller G, Wareham NJ, Langenberg C. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med 2021;27:471-9. [PMID: 33707775 DOI: 10.1038/s41591-021-01266-0] [Cited by in Crossref: 23] [Cited by in F6Publishing: 27] [Article Influence: 23.0] [Reference Citation Analysis]
102 Huber M, Kepesidis KV, Voronina L, Božić M, Trubetskov M, Harbeck N, Krausz F, Žigman M. Stability of person-specific blood-based infrared molecular fingerprints opens up prospects for health monitoring. Nat Commun 2021;12:1511. [PMID: 33686065 DOI: 10.1038/s41467-021-21668-5] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 12.0] [Reference Citation Analysis]
103 Yano Y, Niiranen TJ. Gut Microbiome over a Lifetime and the Association with Hypertension. Curr Hypertens Rep 2021;23:15. [PMID: 33686539 DOI: 10.1007/s11906-021-01133-w] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
104 Zhang Y, Yu H, Dong R, Ji X, Li F. Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis. Biomed Res Int 2021;2021:5592472. [PMID: 33763475 DOI: 10.1155/2021/5592472] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
105 Paquette AG, Hood L, Price ND, Sadovsky Y. Deep phenotyping during pregnancy for predictive and preventive medicine. Sci Transl Med 2020;12:eaay1059. [PMID: 31969484 DOI: 10.1126/scitranslmed.aay1059] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 15.0] [Reference Citation Analysis]
106 Ashbrook DG, Arends D, Prins P, Mulligan MK, Roy S, Williams EG, Lutz CM, Valenzuela A, Bohl CJ, Ingels JF, McCarty MS, Centeno AG, Hager R, Auwerx J, Lu L, Williams RW. A platform for experimental precision medicine: The extended BXD mouse family. Cell Syst 2021;12:235-247.e9. [PMID: 33472028 DOI: 10.1016/j.cels.2020.12.002] [Cited by in Crossref: 53] [Cited by in F6Publishing: 54] [Article Influence: 53.0] [Reference Citation Analysis]
107 Phillip JM, Zamponi N, Phillip MP, Daya J, McGovern S, Williams W, Tschudi K, Jayatilaka H, Wu PH, Walston J, Wirtz D. Fractional re-distribution among cell motility states during ageing. Commun Biol 2021;4:81. [PMID: 33469145 DOI: 10.1038/s42003-020-01605-w] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
108 Mias GI, Singh VV, Rogers LRK, Xue S, Zheng M, Domanskyi S, Kanada M, Piermarocchi C, He J. Longitudinal saliva omics responses to immune perturbation: a case study. Sci Rep 2021;11:710. [PMID: 33436912 DOI: 10.1038/s41598-020-80605-6] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 8.0] [Reference Citation Analysis]
109 Valadez-Barba V, Cota-Coronado A, Hernández-Pérez OR, Lugo-Fabres PH, Padilla-Camberos E, Díaz NF, Díaz-Martínez NE. iPSC for modeling neurodegenerative disorders. Regen Ther 2020;15:332-9. [PMID: 33426236 DOI: 10.1016/j.reth.2020.11.006] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 8.0] [Reference Citation Analysis]
110 Chin A, Rider NL. Artificial Intelligence in Clinical Immunology. Artificial Intelligence in Medicine 2021. [DOI: 10.1007/978-3-030-58080-3_83-1] [Reference Citation Analysis]
111 Tung J, Gower S, Ooteghem KV, Nouredanesh M, Gage WH. Point of care TECHNOLOGIES. Digital Health 2021. [DOI: 10.1016/b978-0-12-818914-6.00008-9] [Reference Citation Analysis]
112 Zhang X, Wang J, Liu B, Yao H, Chen Y, Yin Y, Yang X, Li L. Potential mechanism of Huatan Qushi decoction on improving phlegm-dampness constitution using microRNA array and RT-qPCR targeting on hsa-miR-1237–3p. Journal of Traditional Chinese Medical Sciences 2021;8:43-51. [DOI: 10.1016/j.jtcms.2021.01.007] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
113 Kubassova O, Shaikh F, Melus C, Mahler M. History, current status, and future directions of artificial intelligence. Precision Medicine and Artificial Intelligence 2021. [DOI: 10.1016/b978-0-12-820239-5.00002-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
114 Omics, Informatics, and Precision Medicine. Goodman's Medical Cell Biology 2021. [DOI: 10.1016/b978-0-12-817927-7.00014-4] [Reference Citation Analysis]
115 Jagannathan R, Tamura K, Vellanki P. Diabetes mellitus: diagnosis and heterogeneity. Reference Module in Food Science 2021. [DOI: 10.1016/b978-0-12-821848-8.00035-4] [Reference Citation Analysis]
116 Lin Y, Bariya M, Javey A. Wearable Biosensors for Body Computing. Adv Funct Mater 2021;31:2008087. [DOI: 10.1002/adfm.202008087] [Cited by in Crossref: 25] [Cited by in F6Publishing: 26] [Article Influence: 12.5] [Reference Citation Analysis]
117 López-Otín C, Kroemer G. Hallmarks of Health. Cell 2021;184:33-63. [PMID: 33340459 DOI: 10.1016/j.cell.2020.11.034] [Cited by in Crossref: 124] [Cited by in F6Publishing: 141] [Article Influence: 62.0] [Reference Citation Analysis]
118 Henze L, Walter U, Murua Escobar H, Junghanss C, Jaster R, Köhling R, Lange F, Salehzadeh-Yazdi A, Wolkenhauer O, Hamed M, Barrantes I, Palmer D, Möller S, Kowald A, Heussen N, Fuellen G. Towards biomarkers for outcomes after pancreatic ductal adenocarcinoma and ischaemic stroke, with focus on (co)-morbidity and ageing/cellular senescence (SASKit): protocol for a prospective cohort study. BMJ Open 2020;10:e039560. [PMID: 33334830 DOI: 10.1136/bmjopen-2020-039560] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
119 Contrepois K, Wu S, Moneghetti KJ, Hornburg D, Ahadi S, Tsai MS, Metwally AA, Wei E, Lee-McMullen B, Quijada JV, Chen S, Christle JW, Ellenberger M, Balliu B, Taylor S, Durrant MG, Knowles DA, Choudhry H, Ashland M, Bahmani A, Enslen B, Amsallem M, Kobayashi Y, Avina M, Perelman D, Schüssler-Fiorenza Rose SM, Zhou W, Ashley EA, Montgomery SB, Chaib H, Haddad F, Snyder MP. Molecular Choreography of Acute Exercise. Cell 2020;181:1112-1130.e16. [PMID: 32470399 DOI: 10.1016/j.cell.2020.04.043] [Cited by in Crossref: 127] [Cited by in F6Publishing: 95] [Article Influence: 63.5] [Reference Citation Analysis]
120 Popovic D, Schiltz K, Falkai P, Koutsouleris N. Präzisionspsychiatrie und der Beitrag von Brain Imaging und anderen Biomarkern. Fortschr Neurol Psychiatr 2020;88:778-85. [PMID: 33307561 DOI: 10.1055/a-1300-2162] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
121 Subramanian M, Wojtusciszyn A, Favre L, Boughorbel S, Shan J, Letaief KB, Pitteloud N, Chouchane L. Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med 2020;18:472. [PMID: 33298113 DOI: 10.1186/s12967-020-02658-5] [Cited by in Crossref: 28] [Cited by in F6Publishing: 33] [Article Influence: 14.0] [Reference Citation Analysis]
122 Liu Y, Li N, Zhu X, Qi Y. How wide is the application of genetic big data in biomedicine. Biomed Pharmacother 2021;133:111074. [PMID: 33378973 DOI: 10.1016/j.biopha.2020.111074] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
123 Pulendran B, Davis MM. The science and medicine of human immunology. Science 2020;369:eaay4014. [PMID: 32973003 DOI: 10.1126/science.aay4014] [Cited by in Crossref: 77] [Cited by in F6Publishing: 79] [Article Influence: 38.5] [Reference Citation Analysis]
124 Seiler A, von Känel R, Slavich GM. The Psychobiology of Bereavement and Health: A Conceptual Review From the Perspective of Social Signal Transduction Theory of Depression. Front Psychiatry 2020;11:565239. [PMID: 33343412 DOI: 10.3389/fpsyt.2020.565239] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
125 Uyar B, Palmer D, Kowald A, Murua Escobar H, Barrantes I, Möller S, Akalin A, Fuellen G. Single-cell analyses of aging, inflammation and senescence. Ageing Res Rev 2020;64:101156. [PMID: 32949770 DOI: 10.1016/j.arr.2020.101156] [Cited by in Crossref: 25] [Cited by in F6Publishing: 21] [Article Influence: 12.5] [Reference Citation Analysis]
126 Zhu F, Pan Z, Tang Y, Fu P, Cheng S, Hou W, Zhang Q, Huang H, Sun Y. Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER. CNS Neurosci Ther 2021;27:92-100. [PMID: 33249760 DOI: 10.1111/cns.13509] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
127 Thapa C, Camtepe S. Precision health data: Requirements, challenges and existing techniques for data security and privacy. Comput Biol Med 2021;129:104130. [PMID: 33271399 DOI: 10.1016/j.compbiomed.2020.104130] [Cited by in Crossref: 31] [Cited by in F6Publishing: 34] [Article Influence: 15.5] [Reference Citation Analysis]
128 Nielsen RL, Helenius M, Garcia SL, Roager HM, Aytan-Aktug D, Hansen LBS, Lind MV, Vogt JK, Dalgaard MD, Bahl MI, Jensen CB, Muktupavela R, Warinner C, Aaskov V, Gøbel R, Kristensen M, Frøkiær H, Sparholt MH, Christensen AF, Vestergaard H, Hansen T, Kristiansen K, Brix S, Petersen TN, Lauritzen L, Licht TR, Pedersen O, Gupta R. Data integration for prediction of weight loss in randomized controlled dietary trials. Sci Rep 2020;10:20103. [PMID: 33208769 DOI: 10.1038/s41598-020-76097-z] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
129 Marabita F, James T, Karhu A, Virtanen H, Kettunen K, Stenlund H, Boulund F, Hellström C, Neiman M, Mills R, Perheentupa T, Laivuori H, Helkkula P, Byrne M, Jokinen I, Honko H, Kallonen A, Ermes M, Similä H, Lindholm M, Widen E, Ripatti S, Perälä-heape M, Engstrand L, Nilsson P, Moritz T, Miettinen T, Sallinen R, Kallioniemi O. Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health.. [DOI: 10.1101/2020.11.11.365387] [Reference Citation Analysis]
130 Velten B, Braunger JM, Arnol D, Argelaguet R, Stegle O. Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO.. [DOI: 10.1101/2020.11.03.366674] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
131 Wen G, Zhou T, Gu W. The potential of using blood circular RNA as liquid biopsy biomarker for human diseases. Protein Cell. [DOI: 10.1007/s13238-020-00799-3] [Cited by in Crossref: 49] [Cited by in F6Publishing: 41] [Article Influence: 24.5] [Reference Citation Analysis]
132 Butte KD, Bahmani A, Butte AJ, Li X, Snyder MP. Five Year Pediatric Use of a Digital Wearable Fitness Device: Lessons from a Pilot Case Study.. [DOI: 10.1101/2020.10.21.20215491] [Reference Citation Analysis]
133 Huang Q, Fang Q, Hu Z. A P4 Medicine Perspective of Gut Microbiota and Prediabetes: Systems Analysis and Personalized Intervention. J Transl Int Med 2020;8:119-30. [PMID: 33062587 DOI: 10.2478/jtim-2020-0020] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
134 Rider NL. Digital systems for improving outcomes in patients with primary immune defects. Curr Opin Pediatr 2020;32:772-9. [PMID: 33060445 DOI: 10.1097/MOP.0000000000000963] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
135 Zanini JC, Pietzner M, Langenberg C. Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes. Curr Diab Rep 2020;20:60. [PMID: 33033935 DOI: 10.1007/s11892-020-01340-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
136 Bravo-Merodio L, Acharjee A, Russ D, Bisht V, Williams JA, Tsaprouni LG, Gkoutos GV. Translational biomarkers in the era of precision medicine. Adv Clin Chem 2021;102:191-232. [PMID: 34044910 DOI: 10.1016/bs.acc.2020.08.002] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
137 Ahmed Z. Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis. Hum Genomics 2020;14:35. [PMID: 33008459 DOI: 10.1186/s40246-020-00287-z] [Cited by in Crossref: 28] [Cited by in F6Publishing: 28] [Article Influence: 14.0] [Reference Citation Analysis]
138 Rider NL, Srinivasan R, Khoury P. Artificial intelligence and the hunt for immunological disorders. Curr Opin Allergy Clin Immunol 2020;20:565-73. [PMID: 33002894 DOI: 10.1097/ACI.0000000000000691] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
139 Petrov MS. Metabolic Trifecta After Pancreatitis: Exocrine Pancreatic Dysfunction, Altered Gut Microbiota, and New-Onset Diabetes. Clin Transl Gastroenterol 2019;10:e00086. [PMID: 31609744 DOI: 10.14309/ctg.0000000000000086] [Cited by in Crossref: 23] [Cited by in F6Publishing: 24] [Article Influence: 11.5] [Reference Citation Analysis]
140 Rhee JW, Ky B, Armenian SH, Yancy CW, Wu JC. Primer on Biomarker Discovery in Cardio-Oncology: Application of Omics Technologies. JACC CardioOncol 2020;2:379-84. [PMID: 33073248 DOI: 10.1016/j.jaccao.2020.07.006] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
141 Lam B, Catt M, Cassidy S, Bacardit J, Darke P, Butterfield S, Alshabrawy O, Trenell M, Missier P. Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning–Based Cross-sectional Study of the UK Biobank Accelerometer Cohort (Preprint).. [DOI: 10.2196/preprints.23364] [Reference Citation Analysis]
142 Myskja BK, Steinsbekk KS. Personalized medicine, digital technology and trust: a Kantian account. Med Health Care Philos 2020;23:577-87. [PMID: 32888101 DOI: 10.1007/s11019-020-09974-z] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
143 Ahmed Z, Zeeshan S, Foran DJ, Kleinman LC, Wondisford FE, Dong X. Integrative clinical, genomics and metabolomics data analysis for mainstream precision medicine to investigate COVID-19. BMJ Innov 2020;7:6-10. [DOI: 10.1136/bmjinnov-2020-000444] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
144 Wu H, Tremaroli V, Schmidt C, Lundqvist A, Olsson LM, Krämer M, Gummesson A, Perkins R, Bergström G, Bäckhed F. The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study. Cell Metabolism 2020;32:379-390.e3. [DOI: 10.1016/j.cmet.2020.06.011] [Cited by in Crossref: 95] [Cited by in F6Publishing: 103] [Article Influence: 47.5] [Reference Citation Analysis]
145 Daniel C, Kalra D; Section Editors for the IMIA Yearbook Section on Clinical Research Informatics. Clinical Research Informatics. Yearb Med Inform 2020;29:203-7. [PMID: 32823317 DOI: 10.1055/s-0040-1702007] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
146 Wainberg M, Magis AT, Earls JC, Lovejoy JC, Sinnott-Armstrong N, Omenn GS, Hood L, Price ND. Multiomic blood correlates of genetic risk identify presymptomatic disease alterations. Proc Natl Acad Sci U S A 2020;117:21813-20. [PMID: 32817414 DOI: 10.1073/pnas.2001429117] [Cited by in Crossref: 11] [Cited by in F6Publishing: 14] [Article Influence: 5.5] [Reference Citation Analysis]
147 Ding X, Wang W, Scheetz J, He M. The Guangzhou Twin Eye Study: 2019 Update. Twin Res Hum Genet 2019;22:492-8. [PMID: 32014069 DOI: 10.1017/thg.2019.118] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
148 Pratap A, Steinhubl S, Neto EC, Wegerich SW, Peterson CT, Weiss L, Patel S, Chopra D, Mills PJ. Changes in Continuous, Long-Term Heart Rate Variability and Individualized Physiological Responses to Wellness and Vacation Interventions Using a Wearable Sensor. Front Cardiovasc Med 2020;7:120. [PMID: 32850982 DOI: 10.3389/fcvm.2020.00120] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
149 Schork NJ, Goetz LH, Lowey J, Trent J. Strategies for Testing Intervention Matching Schemes in Cancer. Clin Pharmacol Ther 2020;108:542-52. [PMID: 32535886 DOI: 10.1002/cpt.1947] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
150 Blume JE, Manning WC, Troiano G, Hornburg D, Figa M, Hesterberg L, Platt TL, Zhao X, Cuaresma RA, Everley PA, Ko M, Liou H, Mahoney M, Ferdosi S, Elgierari EM, Stolarczyk C, Tangeysh B, Xia H, Benz R, Siddiqui A, Carr SA, Ma P, Langer R, Farias V, Farokhzad OC. Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. Nat Commun 2020;11:3662. [PMID: 32699280 DOI: 10.1038/s41467-020-17033-7] [Cited by in Crossref: 72] [Cited by in F6Publishing: 78] [Article Influence: 36.0] [Reference Citation Analysis]
151 Kurnat-Thoma E. Educational and Ethical Considerations for Genetic Test Implementation Within Health Care Systems. Netw Syst Med 2020;3:58-66. [PMID: 32676590 DOI: 10.1089/nsm.2019.0010] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
152 Ferreira P, Pereira ÉJ, Pereira HB. From Big Data to Econophysics and Its Use to Explain Complex Phenomena. JRFM 2020;13:153. [DOI: 10.3390/jrfm13070153] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
153 Cammarota G, Ianiro G, Ahern A, Carbone C, Temko A, Claesson MJ, Gasbarrini A, Tortora G. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nat Rev Gastroenterol Hepatol 2020;17:635-48. [DOI: 10.1038/s41575-020-0327-3] [Cited by in Crossref: 88] [Cited by in F6Publishing: 90] [Article Influence: 44.0] [Reference Citation Analysis]
154 Qian G, Ho JWK. Challenges and emerging systems biology approaches to discover how the human gut microbiome impact host physiology. Biophys Rev 2020;12:851-63. [PMID: 32638331 DOI: 10.1007/s12551-020-00724-2] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
155 Tyler J, Choi SW, Tewari M. Real-time, personalized medicine through wearable sensors and dynamic predictive modeling: a new paradigm for clinical medicine. Curr Opin Syst Biol 2020;20:17-25. [PMID: 32984661 DOI: 10.1016/j.coisb.2020.07.001] [Cited by in Crossref: 25] [Cited by in F6Publishing: 28] [Article Influence: 12.5] [Reference Citation Analysis]
156 Berg G, Rybakova D, Fischer D, Cernava T, Vergès MC, Charles T, Chen X, Cocolin L, Eversole K, Corral GH, Kazou M, Kinkel L, Lange L, Lima N, Loy A, Macklin JA, Maguin E, Mauchline T, McClure R, Mitter B, Ryan M, Sarand I, Smidt H, Schelkle B, Roume H, Kiran GS, Selvin J, Souza RSC, van Overbeek L, Singh BK, Wagner M, Walsh A, Sessitsch A, Schloter M. Microbiome definition re-visited: old concepts and new challenges. Microbiome 2020;8:103. [PMID: 32605663 DOI: 10.1186/s40168-020-00875-0] [Cited by in Crossref: 386] [Cited by in F6Publishing: 417] [Article Influence: 193.0] [Reference Citation Analysis]
157 Mias GI, Singh VV, Rogers LR, Xue S, Zheng M, Domanskyi S, Kanada M, Piermarocchi C, He J. Longitudinal Saliva Omics Responses to Immune Perturbation: A Case Study.. [DOI: 10.1101/2020.06.16.156133] [Reference Citation Analysis]
158 Morrow JD, Castaldi PJ, Chase RP, Yun JH, Lee S, Liu Y, Hersh CP, the COPDGene Investigators. Peripheral blood microbial signatures in COPD.. [DOI: 10.1101/2020.05.31.126367] [Reference Citation Analysis]
159 Meyer DH, Schumacher B. A transcriptome based aging clock near the theoretical limit of accuracy.. [DOI: 10.1101/2020.05.29.123430] [Reference Citation Analysis]
160 Moore JB. From personalised nutrition to precision medicine: the rise of consumer genomics and digital health. Proc Nutr Soc 2020;79:300-10. [PMID: 32468984 DOI: 10.1017/S0029665120006977] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
161 Espinoza JL, Shah N, Singh S, Nelson KE, Dupont CL. Applications of weighted association networks applied to compositional data in biology. Environ Microbiol 2020;22:3020-38. [PMID: 32436334 DOI: 10.1111/1462-2920.15091] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
162 Corbi SCT, de Vasconcellos JF, Bastos AS, Bussaneli DG, da Silva BR, Santos RA, Takahashi CS, de S Rocha C, Carvalho BS, Maurer-Morelli CV, Orrico SRP, Barros SP, Scarel-Caminaga RM. Circulating lymphocytes and monocytes transcriptomic analysis of patients with type 2 diabetes mellitus, dyslipidemia and periodontitis. Sci Rep 2020;10:8145. [PMID: 32424199 DOI: 10.1038/s41598-020-65042-9] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 5.5] [Reference Citation Analysis]
163 Yang X. Multitissue Multiomics Systems Biology to Dissect Complex Diseases. Trends Mol Med 2020;26:718-28. [PMID: 32439301 DOI: 10.1016/j.molmed.2020.04.006] [Cited by in Crossref: 26] [Cited by in F6Publishing: 21] [Article Influence: 13.0] [Reference Citation Analysis]
164 Fiocchi C, Iliopoulos D. What's new in IBD therapy: An "omics network" approach. Pharmacol Res 2020;159:104886. [PMID: 32428668 DOI: 10.1016/j.phrs.2020.104886] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 8.5] [Reference Citation Analysis]
165 Rockne RC, Branciamore S, Qi J, Frankhouser DE, O'Meally D, Hua WK, Cook G, Carnahan E, Zhang L, Marom A, Wu H, Maestrini D, Wu X, Yuan YC, Liu Z, Wang LD, Forman S, Carlesso N, Kuo YH, Marcucci G. State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia. Cancer Res 2020;80:3157-69. [PMID: 32414754 DOI: 10.1158/0008-5472.CAN-20-0354] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 7.5] [Reference Citation Analysis]
166 Zhou X, Johnson JS, Spakowicz D, Zhou W, Zhou Y, Sodergren E, Snyder M, Weinstock GM. Longitudinal Analysis of Serum Cytokine Levels and Gut Microbial Abundance Links IL-17/IL-22 With Clostridia and Insulin Sensitivity in Humans. Diabetes 2020;69:1833-42. [PMID: 32366680 DOI: 10.2337/db19-0592] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
167 Phillip JM, Zamponi N, Phillip MP, Daya J, Mcgovern S, Williams W, Tschudi K, Jayatilaka H, Wu P, Walston J, Wirtz D. Fractional re-distribution among cell motility states during ageing.. [DOI: 10.1101/2020.04.29.069286] [Reference Citation Analysis]
168 Ekroos K, Lavrynenko O, Titz B, Pater C, Hoeng J, Ivanov NV. Lipid-based biomarkers for CVD, COPD, and aging - A translational perspective. Prog Lipid Res 2020;78:101030. [PMID: 32339553 DOI: 10.1016/j.plipres.2020.101030] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
169 Philipson LH. Harnessing heterogeneity in type 2 diabetes mellitus. Nat Rev Endocrinol. 2020;16:79-80. [PMID: 31831872 DOI: 10.1038/s41574-019-0308-1] [Cited by in Crossref: 13] [Cited by in F6Publishing: 15] [Article Influence: 6.5] [Reference Citation Analysis]
170 Henze L, Walter U, Escobar HM, Junghanß C, Jaster R, Köhling R, Lange F, Salehzadeh-yazdi A, Wolkenhauer O, Hamed M, Barrantes I, Palmer D, Möller S, Kowald A, Heussen N, Fuellen G. Towards biomarkers for outcomes after pancreatic ductal adenocarcinoma and ischemic stroke, with focus on (co-)morbidity and aging / cellular senescence (SASKit): protocol for a prospective cohort study.. [DOI: 10.1101/2020.04.09.20037010] [Reference Citation Analysis]
171 Gerussi A, Lucà M, Cristoferi L, Ronca V, Mancuso C, Milani C, D'Amato D, O'Donnell SE, Carbone M, Invernizzi P. New Therapeutic Targets in Autoimmune Cholangiopathies. Front Med (Lausanne) 2020;7:117. [PMID: 32318580 DOI: 10.3389/fmed.2020.00117] [Cited by in Crossref: 16] [Cited by in F6Publishing: 17] [Article Influence: 8.0] [Reference Citation Analysis]
172 Zheng M, Domanskyi S, Piermarocchi C, Mias GI. Visibility Graph Based Community Detection for Biological Time Series.. [DOI: 10.1101/2020.03.02.973263] [Reference Citation Analysis]
173 Genkel VV, Shaposhnik II. Conceptualization of Heterogeneity of Chronic Diseases and Atherosclerosis as a Pathway to Precision Medicine: Endophenotype, Endotype, and Residual Cardiovascular Risk. Int J Chronic Dis 2020;2020:5950813. [PMID: 32099839 DOI: 10.1155/2020/5950813] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
174 Quer G, Gouda P, Galarnyk M, Topol EJ, Steinhubl SR. Inter- and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI, and time of year: Retrospective, longitudinal cohort study of 92,457 adults. PLoS One 2020;15:e0227709. [PMID: 32023264 DOI: 10.1371/journal.pone.0227709] [Cited by in Crossref: 64] [Cited by in F6Publishing: 68] [Article Influence: 32.0] [Reference Citation Analysis]
175 Peterson LS, Stelzer IA, Tsai AS, Ghaemi MS, Han X, Ando K, Winn VD, Martinez NR, Contrepois K, Moufarrej MN, Quake S, Relman DA, Snyder MP, Shaw GM, Stevenson DK, Wong RJ, Arck P, Angst MS, Aghaeepour N, Gaudilliere B. Multiomic immune clockworks of pregnancy. Semin Immunopathol 2020;42:397-412. [PMID: 32020337 DOI: 10.1007/s00281-019-00772-1] [Cited by in Crossref: 28] [Cited by in F6Publishing: 24] [Article Influence: 14.0] [Reference Citation Analysis]
176 Wang C, Hu J, Blaser MJ, Li H. Microbial trend analysis for common dynamic trend, group comparison and classification in longitudinal microbiome study.. [DOI: 10.1101/2020.01.30.926824] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
177 Long NP, Nghi TD, Kang YP, Anh NH, Kim HM, Park SK, Kwon SW. Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine. Metabolites 2020;10:E51. [PMID: 32013105 DOI: 10.3390/metabo10020051] [Cited by in Crossref: 35] [Cited by in F6Publishing: 35] [Article Influence: 17.5] [Reference Citation Analysis]
178 Cazassa MJ, Oliveira MDS, Spahr CM, Shields GS, Slavich GM. The Stress and Adversity Inventory for Adults (Adult STRAIN) in Brazilian Portuguese: Initial Validation and Links With Executive Function, Sleep, and Mental and Physical Health. Front Psychol 2019;10:3083. [PMID: 32063871 DOI: 10.3389/fpsyg.2019.03083] [Cited by in Crossref: 21] [Cited by in F6Publishing: 22] [Article Influence: 10.5] [Reference Citation Analysis]
179 Mussap M, Loddo C, Fanni C, Fanos V. Metabolomics in pharmacology - a delve into the novel field of pharmacometabolomics. Expert Rev Clin Pharmacol 2020;13:115-34. [PMID: 31958027 DOI: 10.1080/17512433.2020.1713750] [Cited by in Crossref: 16] [Cited by in F6Publishing: 8] [Article Influence: 8.0] [Reference Citation Analysis]
180 Contrepois K, Wu S, Moneghetti KJ, Hornburg D, Ahadi S, Tsai M, Metwally AA, Wei E, Lee-mcmullen B, Quijada JV, Chen S, Christle JW, Ellenberger M, Balliu B, Taylor S, Durrant M, Knowles DA, Choudhry H, Ashland M, Bahmani A, Enslen B, Amsallem M, Kobayashi Y, Avina M, Perelman D, Rose SMS, Zhou W, Ashley EA, Montgomery SB, Chaib H, Haddad F, Snyder MP. Molecular Choreography of Acute Exercise.. [DOI: 10.1101/2020.01.13.905349] [Reference Citation Analysis]
181 Ahadi S, Zhou W, Schüssler-Fiorenza Rose SM, Sailani MR, Contrepois K, Avina M, Ashland M, Brunet A, Snyder M. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat Med 2020;26:83-90. [PMID: 31932806 DOI: 10.1038/s41591-019-0719-5] [Cited by in Crossref: 119] [Cited by in F6Publishing: 123] [Article Influence: 59.5] [Reference Citation Analysis]
182 . Why Loneliness Interventions Are Unsuccessful: A Call for Precision Health. Adv Geriatr Med Res 2020. [DOI: 10.20900/agmr20200016] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
183 Laserna Mendieta EJ, Mondéjar García R, Varo Sánchez GM, Sanz Martín P, Molina Romero M, Orera Clemente M. Precision medicine in clinical laboratories in Spain: results from a survey addressed to Laboratory Medicine specialists. Rev Med Lab 2020. [DOI: 10.20960/revmedlab.00034] [Reference Citation Analysis]
184 Wang C, Zhao P, Sun S, Teckman J, Balch WE. Leveraging Population Genomics for Individualized Correction of the Hallmarks of Alpha-1 Antitrypsin Deficiency. Chronic Obstr Pulm Dis 2020;7:224-46. [PMID: 32726074 DOI: 10.15326/jcopdf.7.3.2019.0167] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
185 Bunning BJ, Contrepois K, Lee-McMullen B, Dhondalay GKR, Zhang W, Tupa D, Raeber O, Desai M, Nadeau KC, Snyder MP, Andorf S. Global metabolic profiling to model biological processes of aging in twins. Aging Cell 2020;19:e13073. [PMID: 31746094 DOI: 10.1111/acel.13073] [Cited by in Crossref: 18] [Cited by in F6Publishing: 24] [Article Influence: 9.0] [Reference Citation Analysis]
186 Vogt H, Green S. Personalised Medicine: Problems of Translation into the Human Domain. De-Sequencing 2020. [DOI: 10.1007/978-981-15-7728-4_2] [Reference Citation Analysis]
187 Zhao J, Wang Y, Zhao D, Zhang L, Chen P, Xu X. Integration of metabolomics and proteomics to reveal the metabolic characteristics of high-intensity interval training. Analyst 2020;145:6500-10. [DOI: 10.1039/d0an01287d] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
188 Gerussi A, D’amato D, Cristoferi L, O’donnell SE, Carbone M, Invernizzi P. Multiple therapeutic targets in rare cholestatic liver diseases: Time to redefine treatment strategies. Annals of Hepatology 2020;19:5-16. [DOI: 10.1016/j.aohep.2019.09.009] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
189 Misra BB, Misra A. The chemical exposome of type 2 diabetes mellitus: Opportunities and challenges in the omics era. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2020;14:23-38. [DOI: 10.1016/j.dsx.2019.12.001] [Cited by in Crossref: 17] [Cited by in F6Publishing: 14] [Article Influence: 8.5] [Reference Citation Analysis]
190 Prater MR. Teaching Millennials and Generation Z. Advances in Medical Education, Research, and Ethics 2019. [DOI: 10.4018/978-1-7998-1468-9.ch004] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
191 Fu MR, Kurnat-Thoma E, Starkweather A, Henderson WA, Cashion AK, Williams JK, Katapodi MC, Reuter-Rice K, Hickey KT, Barcelona de Mendoza V, Calzone K, Conley YP, Anderson CM, Lyon DE, Weaver MT, Shiao PK, Constantino RE, Wung SF, Hammer MJ, Voss JG, Coleman B. Precision health: A nursing perspective. Int J Nurs Sci 2020;7:5-12. [PMID: 32099853 DOI: 10.1016/j.ijnss.2019.12.008] [Cited by in Crossref: 15] [Cited by in F6Publishing: 17] [Article Influence: 5.0] [Reference Citation Analysis]
192 Kappel BA, Federici M. Gut microbiome and cardiometabolic risk. Rev Endocr Metab Disord 2019;20:399-406. [DOI: 10.1007/s11154-019-09533-9] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 4.3] [Reference Citation Analysis]
193 Liu Y, Vu V, Sweeney G. Examining the Potential of Developing and Implementing Use of Adiponectin-Targeted Therapeutics for Metabolic and Cardiovascular Diseases. Front Endocrinol (Lausanne) 2019;10:842. [PMID: 31920962 DOI: 10.3389/fendo.2019.00842] [Cited by in Crossref: 27] [Cited by in F6Publishing: 30] [Article Influence: 9.0] [Reference Citation Analysis]
194 Furman D, Campisi J, Verdin E, Carrera-Bastos P, Targ S, Franceschi C, Ferrucci L, Gilroy DW, Fasano A, Miller GW, Miller AH, Mantovani A, Weyand CM, Barzilai N, Goronzy JJ, Rando TA, Effros RB, Lucia A, Kleinstreuer N, Slavich GM. Chronic inflammation in the etiology of disease across the life span. Nat Med 2019;25:1822-32. [PMID: 31806905 DOI: 10.1038/s41591-019-0675-0] [Cited by in Crossref: 1084] [Cited by in F6Publishing: 1138] [Article Influence: 361.3] [Reference Citation Analysis]
195 Fishman CE, Mohebnasab M, van Setten J, Zanoni F, Wang C, Deaglio S, Amoroso A, Callans L, van Gelder T, Lee S, Kiryluk K, Lanktree MB, Keating BJ. Genome-Wide Study Updates in the International Genetics and Translational Research in Transplantation Network (iGeneTRAiN). Front Genet 2019;10:1084. [PMID: 31803228 DOI: 10.3389/fgene.2019.01084] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
196 Peixoto RS, Sweet M, Bourne DG. Customized Medicine for Corals. Front Mar Sci 2019;6:686. [DOI: 10.3389/fmars.2019.00686] [Cited by in Crossref: 25] [Cited by in F6Publishing: 25] [Article Influence: 8.3] [Reference Citation Analysis]
197 Miller IJ, Peters SR, Overmyer KA, Paulson BR, Westphall MS, Coon JJ. Real-time health monitoring through urine metabolomics. NPJ Digit Med 2019;2:109. [PMID: 31728416 DOI: 10.1038/s41746-019-0185-y] [Cited by in Crossref: 23] [Cited by in F6Publishing: 23] [Article Influence: 7.7] [Reference Citation Analysis]
198 Yurkovich JT, Tian Q, Price ND, Hood L. A systems approach to clinical oncology uses deep phenotyping to deliver personalized care. Nat Rev Clin Oncol 2020;17:183-94. [DOI: 10.1038/s41571-019-0273-6] [Cited by in Crossref: 25] [Cited by in F6Publishing: 25] [Article Influence: 8.3] [Reference Citation Analysis]
199 Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The Need for Multi-Omics Biomarker Signatures in Precision Medicine. Int J Mol Sci 2019;20:E4781. [PMID: 31561483 DOI: 10.3390/ijms20194781] [Cited by in Crossref: 154] [Cited by in F6Publishing: 163] [Article Influence: 51.3] [Reference Citation Analysis]
200 Vogt H, Green S, Ekstrøm CT, Brodersen J. How precision medicine and screening with big data could increase overdiagnosis. BMJ. [DOI: 10.1136/bmj.l5270] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 5.7] [Reference Citation Analysis]
201 Snyder M, Zhou W. Big data and health. Lancet Digit Health 2019;1:e252-4. [PMID: 33323249 DOI: 10.1016/S2589-7500(19)30109-8] [Cited by in Crossref: 13] [Cited by in F6Publishing: 5] [Article Influence: 4.3] [Reference Citation Analysis]
202 Hatzopoulos AK. Disease Models & Mechanisms in the Age of Big Data. Dis Model Mech 2019;12:dmm041699. [PMID: 31439575 DOI: 10.1242/dmm.041699] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
203 Di Marzo V, Silvestri C. Lifestyle and Metabolic Syndrome: Contribution of the Endocannabinoidome. Nutrients 2019;11:E1956. [PMID: 31434293 DOI: 10.3390/nu11081956] [Cited by in Crossref: 64] [Cited by in F6Publishing: 65] [Article Influence: 21.3] [Reference Citation Analysis]
204 Loscalzo J. Network medicine and type 2 diabetes mellitus: insights into disease mechanism and guide to precision medicine. Endocrine 2019;66:456-9. [PMID: 31410748 DOI: 10.1007/s12020-019-02042-4] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
205 Choi MY, Fritzler MJ. Autoantibodies in SLE: prediction and the p value matrix. Lupus 2019;28:1285-93. [PMID: 31399014 DOI: 10.1177/0961203319868531] [Cited by in Crossref: 18] [Cited by in F6Publishing: 21] [Article Influence: 6.0] [Reference Citation Analysis]
206 Muse ED, Topol EJ. Digital orthodoxy of human data collection. The Lancet 2019;394:556. [DOI: 10.1016/s0140-6736(19)31727-1] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
207 O'Connor MJ, Snyder EA, Hayes FJ. Klinefelter Syndrome and Diabetes. Curr Diab Rep 2019;19:71. [PMID: 31367971 DOI: 10.1007/s11892-019-1197-3] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 5.0] [Reference Citation Analysis]
208 Slavich GM, Sacher J. Stress, sex hormones, inflammation, and major depressive disorder: Extending Social Signal Transduction Theory of Depression to account for sex differences in mood disorders. Psychopharmacology (Berl) 2019;236:3063-79. [PMID: 31359117 DOI: 10.1007/s00213-019-05326-9] [Cited by in Crossref: 108] [Cited by in F6Publishing: 92] [Article Influence: 36.0] [Reference Citation Analysis]
209 Steinhubl SR. The future of individualized health maintenance. Nat Med 2019;25:712-4. [PMID: 31068704 DOI: 10.1038/s41591-019-0443-1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
210 Quer G, Muse ED, Topol EJ, Steinhubl SR. Long data from the electrocardiogram. Lancet 2019;393:2189. [PMID: 31162070 DOI: 10.1016/S0140-6736(19)31186-9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
211 Zhou W, Sailani MR, Contrepois K, Zhou Y, Ahadi S, Leopold SR, Zhang MJ, Rao V, Avina M, Mishra T, Johnson J, Lee-McMullen B, Chen S, Metwally AA, Tran TDB, Nguyen H, Zhou X, Albright B, Hong BY, Petersen L, Bautista E, Hanson B, Chen L, Spakowicz D, Bahmani A, Salins D, Leopold B, Ashland M, Dagan-Rosenfeld O, Rego S, Limcaoco P, Colbert E, Allister C, Perelman D, Craig C, Wei E, Chaib H, Hornburg D, Dunn J, Liang L, Rose SMS, Kukurba K, Piening B, Rost H, Tse D, McLaughlin T, Sodergren E, Weinstock GM, Snyder M. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature. 2019;569:663-671. [PMID: 31142858 DOI: 10.1038/s41586-019-1236-x] [Cited by in Crossref: 257] [Cited by in F6Publishing: 264] [Article Influence: 85.7] [Reference Citation Analysis]
212 Jie Z, Liang S, Ding Q, Li F, Tang S, Wang D, Lin Y, Chen P, Cai K, Qiu X, Li Q, Liao Y, Zhou D, Lian H, Zuo Y, Chen X, Rao W, Ren Y, Wang Y, Zi J, Wang R, Zhou H, Lu H, Wang X, Zhang W, Zhang T, Xiao L, Zong Y, Liu W, Yang H, Wang J, Hou Y, Liu X, Kristiansen K, Zhong H, Jia H, Xu X. A multi-omic cohort as a reference point for promoting a healthy human gut microbiome.. [DOI: 10.1101/585893] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
213 Tsagris M, Tsamardinos I. Feature selection with the R package MXM. F1000Res 2018;7:1505. [PMID: 31656581 DOI: 10.12688/f1000research.16216.2] [Cited by in Crossref: 7] [Cited by in F6Publishing: 10] [Article Influence: 1.8] [Reference Citation Analysis]
214 Rockne RC, Branciamore S, Qi J, Frankhouser D, O’meally D, Hua W, Cook GJ, Carnahan E, Zhang L, Marom A, Wu H, Maestrini D, Wu X, Yuan Y, Liu Z, Wang LD, Forman SJ, Carlesso N, Kuo Y, Marcucci G. State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia Development.. [DOI: 10.1101/238923] [Reference Citation Analysis]
215 [DOI: 10.1101/827071] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Reference Citation Analysis]