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For: Goecks J, Jalili V, Heiser LM, Gray JW. How Machine Learning Will Transform Biomedicine. Cell 2020;181:92-101. [PMID: 32243801 DOI: 10.1016/j.cell.2020.03.022] [Cited by in Crossref: 46] [Cited by in F6Publishing: 32] [Article Influence: 23.0] [Reference Citation Analysis]
Number Citing Articles
1 Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2021. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Wei C, Zhang L, Feng Y, Ma A, Kang Y. Machine learning model for predicting acute kidney injury progression in critically ill patients. BMC Med Inform Decis Mak 2022;22. [DOI: 10.1186/s12911-021-01740-2] [Reference Citation Analysis]
3 Jia X, Yang L, Li C, Xu Y, Yang Q, Chen F. Combining comparative genomic analysis with machine learning reveals some promising diagnostic markers to identify five common pathogenic non-tuberculous mycobacteria. Microb Biotechnol 2021;14:1539-49. [PMID: 34019733 DOI: 10.1111/1751-7915.13815] [Reference Citation Analysis]
4 Semmler G, Wernly S, Wernly B, Mamandipoor B, Bachmayer S, Semmler L, Aigner E, Datz C, Osmani V. Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma. J Pers Med 2021;11:981. [PMID: 34683122 DOI: 10.3390/jpm11100981] [Reference Citation Analysis]
5 Alarcón-zendejas AP, Scavuzzo A, Jiménez-ríos MA, Álvarez-gómez RM, Montiel-manríquez R, Castro-hernández C, Jiménez-dávila MA, Pérez-montiel D, González-barrios R, Jiménez-trejo F, Arriaga-canon C, Herrera LA. The promising role of new molecular biomarkers in prostate cancer: from coding and non-coding genes to artificial intelligence approaches. Prostate Cancer Prostatic Dis. [DOI: 10.1038/s41391-022-00537-2] [Reference Citation Analysis]
6 Kaneko S, Mitsuyama T, Shiraishi K, Ikawa N, Shozu K, Dozen A, Machino H, Asada K, Komatsu M, Kukita A, Sone K, Yoshida H, Motoi N, Hayami S, Yoneoka Y, Kato T, Kohno T, Natsume T, Keudell GV, Saloura V, Yamaue H, Hamamoto R. Genome-Wide Chromatin Analysis of FFPE Tissues Using a Dual-Arm Robot with Clinical Potential. Cancers (Basel) 2021;13:2126. [PMID: 33924956 DOI: 10.3390/cancers13092126] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 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: 31] [Cited by in F6Publishing: 25] [Article Influence: 15.5] [Reference Citation Analysis]
8 Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomedicine & Pharmacotherapy 2020;128:110255. [DOI: 10.1016/j.biopha.2020.110255] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 9.5] [Reference Citation Analysis]
9 Cui ZL, Kadziola Z, Lipkovich I, Faries DE, Sheffield KM, Carter GC. Predicting optimal treatment regimens for patients with HR+/HER2- breast cancer using machine learning based on electronic health records. J Comp Eff Res 2021;10:777-95. [PMID: 33980048 DOI: 10.2217/cer-2020-0230] [Reference Citation Analysis]
10 [DOI: 10.1101/2020.04.17.20069187] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 Laborde CM, Larzabal L, González-Cantero Á, Castro-Santos P, Díaz-Peña R. Advances of Genomic Medicine in Psoriatic Arthritis. J Pers Med 2022;12:35. [PMID: 35055350 DOI: 10.3390/jpm12010035] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Jing Y, Yang J, Johnson DB, Moslehi JJ, Han L. Harnessing big data to characterize immune-related adverse events. Nat Rev Clin Oncol 2022. [PMID: 35039679 DOI: 10.1038/s41571-021-00597-8] [Reference Citation Analysis]
13 Williams S, Layard Horsfall H, Funnell JP, Hanrahan JG, Khan DZ, Muirhead W, Stoyanov D, Marcus HJ. Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm. Cancers (Basel) 2021;13:5010. [PMID: 34638495 DOI: 10.3390/cancers13195010] [Reference Citation Analysis]
14 Nwanosike EM, Conway BR, Merchant HA, Hasan SS. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. Int J Med Inform 2021;159:104679. [PMID: 34990939 DOI: 10.1016/j.ijmedinf.2021.104679] [Reference Citation Analysis]
15 Kim SC, Arun AS, Ahsen ME, Vogel R, Stolovitzky G. The Fermi-Dirac distribution provides a calibrated probabilistic output for binary classifiers. Proc Natl Acad Sci U S A 2021;118:e2100761118. [PMID: 34413191 DOI: 10.1073/pnas.2100761118] [Reference Citation Analysis]
16 Cheng Y, Chen C, Yang J, Yang H, Fu M, Zhong X, Wang B, He M, Hu Z, Zhang Z, Jin X, Kang Y, Wu Q. Using Machine Learning Algorithms to Predict Hospital Acquired Thrombocytopenia after Operation in the Intensive Care Unit: A Retrospective Cohort Study. Diagnostics (Basel) 2021;11:1614. [PMID: 34573956 DOI: 10.3390/diagnostics11091614] [Reference Citation Analysis]
17 Zhong S, Zhang K, Bagheri M, Burken JG, Gu A, Li B, Ma X, Marrone BL, Ren ZJ, Schrier J, Shi W, Tan H, Wang T, Wang X, Wong BM, Xiao X, Yu X, Zhu JJ, Zhang H. Machine Learning: New Ideas and Tools in Environmental Science and Engineering. Environ Sci Technol 2021. [PMID: 34403250 DOI: 10.1021/acs.est.1c01339] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Su X, Jing G, Zhang Y, Wu S. Method development for cross-study microbiome data mining: Challenges and opportunities. Comput Struct Biotechnol J 2020;18:2075-80. [PMID: 32802279 DOI: 10.1016/j.csbj.2020.07.020] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 4.5] [Reference Citation Analysis]
19 Li A, Bergan RC. Clinical trial design: Past, present, and future in the context of big data and precision medicine. Cancer 2020;126:4838-46. [PMID: 32931022 DOI: 10.1002/cncr.33205] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 4.5] [Reference Citation Analysis]
20 Mann M, Kumar C, Zeng WF, Strauss MT. Artificial intelligence for proteomics and biomarker discovery. Cell Syst 2021;12:759-70. [PMID: 34411543 DOI: 10.1016/j.cels.2021.06.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Li Y, Wu Y, Gao Y, Niu X, Li J, Tang M, Fu C, Qi R, Song B, Chen H, Gao X, Yang Y, Guan X. Machine-learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study. BMC Infect Dis 2022;22. [DOI: 10.1186/s12879-022-07125-8] [Reference Citation Analysis]
22 Dutta S, Bose K. Remodelling structure-based drug design using machine learning. Emerg Top Life Sci 2021;5:13-27. [PMID: 33825834 DOI: 10.1042/ETLS20200253] [Reference Citation Analysis]
23 Jamshidi E, Asgary A, Tavakoli N, Zali A, Dastan F, Daaee A, Badakhshan M, Esmaily H, Jamaldini SH, Safari S, Bastanhagh E, Maher A, Babajani A, Mehrazi M, Sendani Kashi MA, Jamshidi M, Sendani MH, Rahi SJ, Mansouri N. Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning. Front Artif Intell 2021;4:673527. [PMID: 34250465 DOI: 10.3389/frai.2021.673527] [Reference Citation Analysis]
24 Liimatainen K, Huttunen R, Latonen L, Ruusuvuori P. Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns. Biomolecules 2021;11:264. [PMID: 33670112 DOI: 10.3390/biom11020264] [Reference Citation Analysis]
25 Hauschild AC, Eick L, Wienbeck J, Heider D. Fostering reproducibility, reusability, and technology transfer in health informatics. iScience 2021;24:102803. [PMID: 34296072 DOI: 10.1016/j.isci.2021.102803] [Reference Citation Analysis]
26 Mansouri M, Hussherr MD, Strittmatter T, Buchmann P, Xue S, Camenisch G, Fussenegger M. Smart-watch-programmed green-light-operated percutaneous control of therapeutic transgenes. Nat Commun 2021;12:3388. [PMID: 34099676 DOI: 10.1038/s41467-021-23572-4] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
27 Gu Q, Kumar A, Bray S, Creason A, Khanteymoori A, Jalili V, Grüning B, Goecks J. Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine. PLoS Comput Biol 2021;17:e1009014. [PMID: 34061826 DOI: 10.1371/journal.pcbi.1009014] [Reference Citation Analysis]
28 Rong F, Xiang H, Qian L, Xue Y, Ji K, Yin R. Machine Learning for Prediction of Outcomes in Cardiogenic Shock. Front Cardiovasc Med 2022;9:849688. [DOI: 10.3389/fcvm.2022.849688] [Reference Citation Analysis]
29 Huang D, Zheng S, Liu Z, Zhu K, Zhi H, Ma G. Machine Learning Revealed Ferroptosis Features and a Novel Ferroptosis-Based Classification for Diagnosis in Acute Myocardial Infarction. Front Genet 2022;13:813438. [DOI: 10.3389/fgene.2022.813438] [Reference Citation Analysis]
30 Liu WC, Li MX, Qian WX, Luo ZW, Liao WJ, Liu ZL, Liu JM. Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer. Cancer Manag Res 2021;13:8723-36. [PMID: 34849027 DOI: 10.2147/CMAR.S330591] [Reference Citation Analysis]
31 Cirillo D, Núñez-Carpintero I, Valencia A. Artificial intelligence in cancer research: learning at different levels of data granularity. Mol Oncol 2021;15:817-29. [PMID: 33533192 DOI: 10.1002/1878-0261.12920] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
32 Wang Y, Lin X, Sun D. A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models? Ann Transl Med 2021;9:1597. [PMID: 34790803 DOI: 10.21037/atm-21-4733] [Reference Citation Analysis]
33 Martínez-garcía M, Hernández-lemus E. Data Integration Challenges for Machine Learning in Precision Medicine. Front Med 2022;8:784455. [DOI: 10.3389/fmed.2021.784455] [Reference Citation Analysis]
34 Rocca A, Kholodenko BN. Can Systems Biology Advance Clinical Precision Oncology? Cancers (Basel) 2021;13:6312. [PMID: 34944932 DOI: 10.3390/cancers13246312] [Reference Citation Analysis]
35 Bongrand P. Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function? CIMB 2022;44:505-25. [DOI: 10.3390/cimb44020035] [Reference Citation Analysis]
36 Glaab E, Rauschenberger A, Banzi R, Gerardi C, Garcia P, Demotes J. Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review. BMJ Open 2021;11:e053674. [PMID: 34873011 DOI: 10.1136/bmjopen-2021-053674] [Reference Citation Analysis]
37 Mao J, Miao J, Lu Y, Tong Z. Machine learning of materials design and state prediction for lithium ion batteries. Chinese Journal of Chemical Engineering 2021;37:1-11. [DOI: 10.1016/j.cjche.2021.04.009] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
38 Ahn J, Kang C, Kim E, Kim A, Kim A. Proteomics for Early Detection of Non-Muscle-Invasive Bladder Cancer: Clinically Useful Urine Protein Biomarkers. Life 2022;12:395. [DOI: 10.3390/life12030395] [Reference Citation Analysis]
39 Castañé H, Baiges-Gaya G, Hernández-Aguilera A, Rodríguez-Tomàs E, Fernández-Arroyo S, Herrero P, Delpino-Rius A, Canela N, Menendez JA, Camps J, Joven J. Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview. Biomolecules 2021;11:473. [PMID: 33810079 DOI: 10.3390/biom11030473] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
40 Li X, Lee EJ, Lilja S, Loscalzo J, Schäfer S, Smelik M, Strobl MR, Sysoev O, Wang H, Zhang H, Zhao Y, Gawel DR, Bohle B, Benson M. A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets. Genome Med 2022;14:48. [PMID: 35513850 DOI: 10.1186/s13073-022-01048-4] [Reference Citation Analysis]
41 Peng Q, Shen Y, Fu K, Dai Z, Jin L, Yang D, Zhu J. Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system. Aging (Albany NY) 2021;13:7361-81. [PMID: 33686949 DOI: 10.18632/aging.202594] [Reference Citation Analysis]
42 Luis-Martínez R, Monje MHG, Antonini A, Sánchez-Ferro Á, Mestre TA. Technology-Enabled Care: Integrating Multidisciplinary Care in Parkinson's Disease Through Digital Technology. Front Neurol 2020;11:575975. [PMID: 33250846 DOI: 10.3389/fneur.2020.575975] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
43 Mansouri M, Xue S, Hussherr MD, Strittmatter T, Camenisch G, Fussenegger M. Smartphone-Flashlight-Mediated Remote Control of Rapid Insulin Secretion Restores Glucose Homeostasis in Experimental Type-1 Diabetes. Small 2021;17:e2101939. [PMID: 34227232 DOI: 10.1002/smll.202101939] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
44 Zhu X, Huang W, Lu H, Wang Z, Ni X, Hu J, Deng S, Tan Y, Li L, Zhang M, Qiu C, Luo Y, Chen H, Huang S, Xiao T, Shang D, Wen Y. A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters. Sci Rep 2021;11:5568. [PMID: 33692435 DOI: 10.1038/s41598-021-85157-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
45 Kucikova L, Danso S, Jia L, Su L. Computational Psychiatry and Computational Neurology: Seeking for Mechanistic Modeling in Cognitive Impairment and Dementia. Front Comput Neurosci 2022;16:865805. [DOI: 10.3389/fncom.2022.865805] [Reference Citation Analysis]
46 Hu R, Hesham AE, Zou Q. Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases. Front Cell Infect Microbiol 2022;12:882995. [DOI: 10.3389/fcimb.2022.882995] [Reference Citation Analysis]
47 Vilne B, Ķibilds J, Siksna I, Lazda I, Valciņa O, Krūmiņa A. Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease. Front Microbiol 2022;13:627892. [DOI: 10.3389/fmicb.2022.627892] [Reference Citation Analysis]
48 Hickey BL, Chen J, Zou Y, Gill AD, Zhong W, Millar JG, Hooley RJ. Enantioselective sensing of insect pheromones in water. Chem Commun (Camb) 2021;57:13341-4. [PMID: 34817473 DOI: 10.1039/d1cc05540b] [Reference Citation Analysis]
49 Scott EN, Hasbullah JS, Carleton BC, Ross CJD. Prevention of adverse drug effects: a pharmacogenomic approach. Curr Opin Pediatr 2020;32:646-53. [PMID: 32796162 DOI: 10.1097/MOP.0000000000000935] [Reference Citation Analysis]
50 Gerdes H, Casado P, Dokal A, Hijazi M, Akhtar N, Osuntola R, Rajeeve V, Fitzgibbon J, Travers J, Britton D, Khorsandi S, Cutillas PR. Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Nat Commun 2021;12:1850. [PMID: 33767176 DOI: 10.1038/s41467-021-22170-8] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 7.0] [Reference Citation Analysis]
51 Shao S, Liu L, Zhao Y, Mu L, Lu Q, Qin J. Application of Machine Learning for Predicting Anastomotic Leakage in Patients with Gastric Adenocarcinoma Who Received Total or Proximal Gastrectomy. J Pers Med 2021;11:748. [PMID: 34442391 DOI: 10.3390/jpm11080748] [Reference Citation Analysis]
52 Sun R, Zhao H, Huang S, Zhang R, Lu Z, Li S, Wang G, Aa J, Xie Y. Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy. Front Pharmacol 2021;12:760474. [PMID: 34916939 DOI: 10.3389/fphar.2021.760474] [Reference Citation Analysis]
53 D'Adamo GL, Widdop JT, Giles EM. The future is now? Clinical and translational aspects of "Omics" technologies. Immunol Cell Biol 2021;99:168-76. [PMID: 32924178 DOI: 10.1111/imcb.12404] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
54 Gasparri R, Sedda G, Spaggiari L. Biomarkers in Early Diagnosis and Early Stage Lung Cancer: The Clinician's Point of View. J Clin Med 2020;9:E1790. [PMID: 32526831 DOI: 10.3390/jcm9061790] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
55 You JB, McCallum C, Wang Y, Riordon J, Nosrati R, Sinton D. Machine learning for sperm selection. Nat Rev Urol 2021;18:387-403. [PMID: 34002070 DOI: 10.1038/s41585-021-00465-1] [Reference Citation Analysis]
56 Chen J, Gill AD, Hickey BL, Gao Z, Cui X, Hooley RJ, Zhong W. Machine Learning Aids Classification and Discrimination of Noncanonical DNA Folding Motifs by an Arrayed Host:Guest Sensing System. J Am Chem Soc 2021;143:12791-9. [PMID: 34346209 DOI: 10.1021/jacs.1c06031] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
57 Ader I, Pénicaud L, Andrieu S, Beard JR, Davezac N, Dray C, Fazilleau N, Gourdy P, Guyonnet S, Liblau R, Parini A, Payoux P, Rampon C, Raymond-Letron I, Rolland Y, de Souto Barreto P, Valet P, Vergnolle N, Sierra F, Vellas B, Casteilla L. Healthy Aging Biomarkers: The INSPIRE's Contribution. J Frailty Aging 2021;10:313-9. [PMID: 34549244 DOI: 10.14283/jfa.2021.15] [Reference Citation Analysis]
58 Liu Z, Lu T, Wang Y, Jiao D, Li Z, Wang L, Liu L, Guo C, Zhao Y, Han X. Establishment and experimental validation of an immune miRNA signature for assessing prognosis and immune landscape of patients with colorectal cancer. J Cell Mol Med 2021;25:6874-86. [PMID: 34101338 DOI: 10.1111/jcmm.16696] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
59 Lavrador P, Gaspar VM, Mano JF. Engineering mammalian living materials towards clinically relevant therapeutics. EBioMedicine 2021;74:103717. [PMID: 34839265 DOI: 10.1016/j.ebiom.2021.103717] [Reference Citation Analysis]
60 Khorsandi SE, Hardgrave HJ, Osborn T, Klutts G, Nigh J, Spencer-Cole RT, Kakos CD, Anastasiou I, Mavros MN, Giorgakis E. Artificial Intelligence in Liver Transplantation. Transplant Proc 2021:S0041-1345(21)00741-7. [PMID: 34740449 DOI: 10.1016/j.transproceed.2021.09.045] [Reference Citation Analysis]
61 Sh Y, Liu B, Zhang J, Zhou Y, Hu Z, Zhang X. Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer's Disease. Front Aging Neurosci 2021;13:768229. [PMID: 34924996 DOI: 10.3389/fnagi.2021.768229] [Reference Citation Analysis]
62 Hu B, Wang C, Jiang K, Shen Z, Yang X, Yin M, Liang B, Xie Q, Ye Y, Gao Z. Development and validation of a novel diagnostic model for initially clinical diagnosed gastrointestinal stromal tumors using an extreme gradient-boosting machine. BMC Gastroenterol 2021;21:481. [PMID: 34922474 DOI: 10.1186/s12876-021-02048-1] [Reference Citation Analysis]
63 Yifan C, Jianfeng S, Jun P. Development and Validation of a Random Forest Diagnostic Model of Acute Myocardial Infarction Based on Ferroptosis-Related Genes in Circulating Endothelial Cells. Front Cardiovasc Med 2021;8:663509. [PMID: 34262953 DOI: 10.3389/fcvm.2021.663509] [Reference Citation Analysis]
64 Ameli N, Gibson MP, Khanna A, Howey M, Lai H. An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes. Front Dent Med 2022;3:833191. [DOI: 10.3389/fdmed.2022.833191] [Reference Citation Analysis]