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For: Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019;11:70. [PMID: 31744524 DOI: 10.1186/s13073-019-0689-8] [Cited by in Crossref: 45] [Cited by in F6Publishing: 34] [Article Influence: 15.0] [Reference Citation Analysis]
Number Citing Articles
1 Sætra HS, Fosch-Villaronga E. Healthcare Digitalisation and the Changing Nature of Work and Society. Healthcare (Basel) 2021;9:1007. [PMID: 34442144 DOI: 10.3390/healthcare9081007] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Saik OV, Klimontov VV. Bioinformatic Reconstruction and Analysis of Gene Networks Related to Glucose Variability in Diabetes and Its Complications. Int J Mol Sci 2020;21:E8691. [PMID: 33217980 DOI: 10.3390/ijms21228691] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
3 Rajasekaran S, Khaniki HB, Ghayesh MH. On the mechanics of shear deformable micro beams under thermo-mechanical loads using finite element analysis and deep learning neural network. Mechanics Based Design of Structures and Machines. [DOI: 10.1080/15397734.2022.2047721] [Reference Citation Analysis]
4 Williams CM, Chaturvedi R, Urman RD, Waterman RS, Gabriel RA. Artificial Intelligence and a Pandemic: an Analysis of the Potential Uses and Drawbacks. J Med Syst 2021;45:26. [PMID: 33459840 DOI: 10.1007/s10916-021-01705-y] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Tanabe S. How can artificial intelligence and humans work together to fight against cancer? Artif Intell Cancer 2020; 1(3): 45-50 [DOI: 10.35713/aic.v1.i3.45] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
6 Henning PA, Henning J, Glück K. Artificial Intelligence: Its future in the health sector and its role for medical education. J Eur CME 2021;10:2014099. [PMID: 34912590 DOI: 10.1080/21614083.2021.2014099] [Reference Citation Analysis]
7 Solomon BD. Can artificial intelligence save medical genetics? Am J Med Genet A 2021. [PMID: 34633139 DOI: 10.1002/ajmg.a.62538] [Reference Citation Analysis]
8 Wen B, Zeng WF, Liao Y, Shi Z, Savage SR, Jiang W, Zhang B. Deep Learning in Proteomics. Proteomics 2020;20:e1900335. [PMID: 32939979 DOI: 10.1002/pmic.201900335] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 4.5] [Reference Citation Analysis]
9 Soler M, Scholtz A, Zeto R, Armani AM. Engineering photonics solutions for COVID-19. APL Photonics 2020;5:090901. [PMID: 33015361 DOI: 10.1063/5.0021270] [Cited by in Crossref: 10] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
10 Scholtz A, Ramoji A, Silge A, Jansson JR, de Moura IG, Popp J, Sram JP, Armani AM. COVID-19 Diagnostics: Past, Present, and Future. ACS Photonics 2021;8:2827-38. [DOI: 10.1021/acsphotonics.1c01052] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
11 Krittanawong C, Johnson KW, Choi E, Kaplin S, Venner E, Murugan M, Wang Z, Glicksberg BS, Amos CI, Schatz MC, Tang W. Artificial Intelligence and Cardiovascular Genetics. Life 2022;12:279. [DOI: 10.3390/life12020279] [Reference Citation Analysis]
12 Austin-Tse CA, Jobanputra V, Perry DL, Bick D, Taft RJ, Venner E, Gibbs RA, Young T, Barnett S, Belmont JW, Boczek N, Chowdhury S, Ellsworth KA, Guha S, Kulkarni S, Marcou C, Meng L, Murdock DR, Rehman AU, Spiteri E, Thomas-Wilson A, Kearney HM, Rehm HL; Medical Genome Initiative*. Best practices for the interpretation and reporting of clinical whole genome sequencing. NPJ Genom Med 2022;7:27. [PMID: 35395838 DOI: 10.1038/s41525-022-00295-z] [Reference Citation Analysis]
13 Santus E, Marino N, Cirillo D, Chersoni E, Montagud A, Santuccione Chadha A, Valencia A, Hughes K, Lindvall C. Artificial Intelligence-Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development. J Med Internet Res 2021;23:e22453. [PMID: 33560998 DOI: 10.2196/22453] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
14 Piccialli F, Somma VD, Giampaolo F, Cuomo S, Fortino G. A survey on deep learning in medicine: Why, how and when? Information Fusion 2021;66:111-37. [DOI: 10.1016/j.inffus.2020.09.006] [Cited by in Crossref: 26] [Cited by in F6Publishing: 4] [Article Influence: 26.0] [Reference Citation Analysis]
15 König H, Frank D, Baumann M, Heil R. AI models and the future of genomic research and medicine: True sons of knowledge?: Artificial intelligence needs to be integrated with causal conceptions in biomedicine to harness its societal benefits for the field. Bioessays 2021;:e2100025. [PMID: 34382215 DOI: 10.1002/bies.202100025] [Reference Citation Analysis]
16 Anderson P, Gadgil R, Johnson WA, Schwab E, Davidson JM. Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge. Comput Biol Med 2021;138:104850. [PMID: 34536702 DOI: 10.1016/j.compbiomed.2021.104850] [Reference Citation Analysis]
17 Cheng X, Wu D, Cheng Y, Qiao T, Wang X. New focuses of clinical and translational medicine in 2020. Clin Transl Med 2020;10:17-9. [PMID: 32508045 DOI: 10.1002/ctm2.9] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Yadav D, Agarwal S, Pancham P, Jindal D, Agarwal V, Dubey PK, Jha SK, Mani S, Rachana, Dey A, Jha NK, Kesari KK, Singh M. Probing the Immune System Dynamics of the COVID-19 Disease for Vaccine Designing and Drug Repurposing Using Bioinformatics Tools. Immuno 2022;2:344-71. [DOI: 10.3390/immuno2020022] [Reference Citation Analysis]
19 Liu J, Zhao H, Zheng Y, Dong L, Zhao S, Huang Y, Huang S, Qian T, Zou J, Liu S, Li J, Yan Z, Li Y, Zhang S, Huang X, Wang W, Li Y, Wang J, Ming Y, Li X, Xing Z, Qin L, Zhao Z, Jia Z, Li J, Liu G, Zhang M, Feng K, Wu J, Zhang J, Yang Y, Wu Z, Liu Z, Ying J, Wang X, Su J, Wang X, Wu N. DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data. Genome Med 2022;14:21. [PMID: 35209950 DOI: 10.1186/s13073-022-01027-9] [Reference Citation Analysis]
20 Bates DW, Levine D, Syrowatka A, Kuznetsova M, Craig KJT, Rui A, Jackson GP, Rhee K. The potential of artificial intelligence to improve patient safety: a scoping review. NPJ Digit Med 2021;4:54. [PMID: 33742085 DOI: 10.1038/s41746-021-00423-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
21 Foo LL, Ng WY, Lim GYS, Tan TE, Ang M, Ting DSW. Artificial intelligence in myopia: current and future trends. Curr Opin Ophthalmol 2021;32:413-24. [PMID: 34310401 DOI: 10.1097/ICU.0000000000000791] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Aoun M, Passerini I, Chiurazzi P, Karali M, De Rienzo I, Sartor G, Murro V, Filimonova N, Seri M, Banfi S. Inherited Retinal Diseases Due to RPE65 Variants: From Genetic Diagnostic Management to Therapy. Int J Mol Sci 2021;22:7207. [PMID: 34281261 DOI: 10.3390/ijms22137207] [Reference Citation Analysis]
23 Liu Y, Gao K, Deng H, Ling T, Lin J, Yu X, Bo X, Zhou J, Gao L, Wang P, Hu J, Zhang J, Tong Z, Liu Y, Shi Y, Ke L, Gao Y, Li W. A time-incorporated SOFA score-based machine learning model for predicting mortality in critically ill patients: A multicenter, real-world study. Int J Med Inform 2022;163:104776. [PMID: 35512625 DOI: 10.1016/j.ijmedinf.2022.104776] [Reference Citation Analysis]
24 Gulfidan G, Beklen H, Arga KY. Artificial Intelligence as Accelerator for Genomic Medicine and Planetary Health. OMICS 2021. [PMID: 34780300 DOI: 10.1089/omi.2021.0170] [Reference Citation Analysis]
25 Alaidarous MA. The emergence of new trends in clinical laboratory diagnosis. Saudi Med J 2020;41:1175-80. [PMID: 33130836 DOI: 10.15537/smj.2020.11.25455] [Reference Citation Analysis]
26 Weeks WB, Huynh G, Cao SY, Smith J, Bangur C, Weinstein JN. Examining the Prevalence of Previously Recorded Phenotypically Related Diagnoses Among Fee-for-Service Medicare Enrollees Newly Diagnosed with Mendelian Conditions. J Gen Intern Med 2021. [PMID: 33479932 DOI: 10.1007/s11606-020-06469-8] [Reference Citation Analysis]
27 Gusic M, Prokisch H. Genetic basis of mitochondrial diseases. FEBS Lett 2021;595:1132-58. [PMID: 33655490 DOI: 10.1002/1873-3468.14068] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
28 Khalifa A, Mason CC, Garvin JH, Williams MS, Del Fiol G, Jackson BR, Bleyl SB, Huff SM. A qualitative study of prevalent laboratory information systems and data communication patterns for genetic test reporting. Genet Med 2021. [PMID: 34230635 DOI: 10.1038/s41436-021-01251-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 De La Vega FM, Chowdhury S, Moore B, Frise E, McCarthy J, Hernandez EJ, Wong T, James K, Guidugli L, Agrawal PB, Genetti CA, Brownstein CA, Beggs AH, Löscher BS, Franke A, Boone B, Levy SE, Õunap K, Pajusalu S, Huentelman M, Ramsey K, Naymik M, Narayanan V, Veeraraghavan N, Billings P, Reese MG, Yandell M, Kingsmore SF. Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases. Genome Med 2021;13:153. [PMID: 34645491 DOI: 10.1186/s13073-021-00965-0] [Reference Citation Analysis]
30 Momozawa Y, Mizukami K. Unique roles of rare variants in the genetics of complex diseases in humans. J Hum Genet 2021;66:11-23. [PMID: 32948841 DOI: 10.1038/s10038-020-00845-2] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
31 Green ED, Gunter C, Biesecker LG, Di Francesco V, Easter CL, Feingold EA, Felsenfeld AL, Kaufman DJ, Ostrander EA, Pavan WJ, Phillippy AM, Wise AL, Dayal JG, Kish BJ, Mandich A, Wellington CR, Wetterstrand KA, Bates SA, Leja D, Vasquez S, Gahl WA, Graham BJ, Kastner DL, Liu P, Rodriguez LL, Solomon BD, Bonham VL, Brody LC, Hutter CM, Manolio TA. Strategic vision for improving human health at The Forefront of Genomics. Nature 2020;586:683-92. [PMID: 33116284 DOI: 10.1038/s41586-020-2817-4] [Cited by in Crossref: 41] [Cited by in F6Publishing: 37] [Article Influence: 20.5] [Reference Citation Analysis]
32 Regan B, O'Kennedy R, Collins D. Advances in point-of-care testing for cardiovascular diseases. Adv Clin Chem 2021;104:1-70. [PMID: 34462053 DOI: 10.1016/bs.acc.2020.09.001] [Reference Citation Analysis]
33 Duong D, Waikel RL, Hu P, Tekendo-Ngongang C, Solomon BD. Neural network classifiers for images of genetic conditions with cutaneous manifestations. HGG Adv 2022;3:100053. [PMID: 35047844 DOI: 10.1016/j.xhgg.2021.100053] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
34 Crone B, Krause AM, Hornsby WE, Willer CJ, Surakka I. Translating genetic association of lipid levels for biological and clinical application. Cardiovasc Drugs Ther 2021;35:617-26. [PMID: 33604704 DOI: 10.1007/s10557-021-07156-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 Afrasiabi A, Keane JT, Heng JI, Palmer EE, Lovell NH, Alinejad-Rokny H. Quantitative neurogenetics: applications in understanding disease. Biochem Soc Trans 2021:BST20200732. [PMID: 34282824 DOI: 10.1042/BST20200732] [Reference Citation Analysis]
36 Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021;10:1048. [PMID: 34451513 DOI: 10.3390/pathogens10081048] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Zhang C, Zhao J, Zhu Z, Li Y, Li K, Wang Y, Zheng Y. Applications of Artificial Intelligence in Myopia: Current and Future Directions. Front Med 2022;9:840498. [DOI: 10.3389/fmed.2022.840498] [Reference Citation Analysis]
38 Yang L, Wei Z, Chen X, Hu L, Peng X, Wang J, Lu C, Kong Y, Dong X, Ni Q, Lu Y, Wu B, Wang H, Meirelles K, Tian X, Zhang J, Chang F, Liu L, Li C, You W, Cheng G, Wang L, Cao Y, Chen C, Fang P, Tang S, Zhou W. Use of medical exome sequencing for identification of underlying genetic defects in NICU: Experience in a cohort of 2303 neonates in China. Clin Genet 2021. [PMID: 34671977 DOI: 10.1111/cge.14075] [Reference Citation Analysis]
39 Sheardown E, Mech AM, Petrazzini MEM, Leggieri A, Gidziela A, Hosseinian S, Sealy IM, Torres-perez JV, Busch-nentwich EM, Malanchini M, Brennan CH. Translational relevance of forward genetic screens in animal models for the study of psychiatric disease. Neuroscience & Biobehavioral Reviews 2022. [DOI: 10.1016/j.neubiorev.2022.104559] [Reference Citation Analysis]
40 Rahman MM, Khatun F, Uzzaman A, Sami SI, Bhuiyan MA, Kiong TS. A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic. Int J Health Serv 2021;51:446-61. [PMID: 33999732 DOI: 10.1177/00207314211017469] [Reference Citation Analysis]
41 Pantel JT, Hajjir N, Danyel M, Elsner J, Abad-Perez AT, Hansen P, Mundlos S, Spielmann M, Horn D, Ott CE, Mensah MA. Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study. J Med Internet Res 2020;22:e19263. [PMID: 33090109 DOI: 10.2196/19263] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
42 Liu Z, Roberts R, Mercer TR, Xu J, Sedlazeck FJ, Tong W. Towards accurate and reliable resolution of structural variants for clinical diagnosis. Genome Biol 2022;23:68. [PMID: 35241127 DOI: 10.1186/s13059-022-02636-8] [Reference Citation Analysis]
43 Ji J, Leung ML, Baker S, Deignan JL, Santani A. Clinical Exome Reanalysis: Current Practice and Beyond. Mol Diagn Ther 2021;25:529-36. [PMID: 34283395 DOI: 10.1007/s40291-021-00541-7] [Reference Citation Analysis]
44 Vlachakis D, Vlamos P. Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents. Math Comput Sci 2021;15:877-88. [DOI: 10.1007/s11786-021-00517-0] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
45 Qian H, Dong B, Yuan JJ, Yin F, Wang Z, Wang HN, Wang HS, Tian D, Li WH, Zhang B, Zhao LB, Ning BT. Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics. Front Med (Lausanne) 2021;8:695185. [PMID: 34820391 DOI: 10.3389/fmed.2021.695185] [Reference Citation Analysis]
46 Malandraki-Miller S, Riley PR. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today 2021;26:887-901. [PMID: 33484947 DOI: 10.1016/j.drudis.2021.01.013] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
47 Bahado-Singh RO, Vishweswaraiah S, Aydas B, Radhakrishna U. Artificial intelligence and placental DNA methylation: newborn prediction and molecular mechanisms of autism in preterm children. J Matern Fetal Neonatal Med 2021;:1-10. [PMID: 34404318 DOI: 10.1080/14767058.2021.1963704] [Reference Citation Analysis]
48 Walter W, Haferlach C, Nadarajah N, Schmidts I, Kühn C, Kern W, Haferlach T. How artificial intelligence might disrupt diagnostics in hematology in the near future. Oncogene 2021;40:4271-80. [PMID: 34103684 DOI: 10.1038/s41388-021-01861-y] [Reference Citation Analysis]
49 Wu D, Chen S, Zhang Y, Zhang H, Wang Q, Li J, Fu Y, Wang S, Yang H, Du H, Zhu H, Pan H, Shen Z. Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition. J Pers Med 2021;11:1172. [PMID: 34834524 DOI: 10.3390/jpm11111172] [Reference Citation Analysis]
50 Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull 2021;139:4-15. [PMID: 34405854 DOI: 10.1093/bmb/ldab016] [Reference Citation Analysis]
51 Jefferies JL, Spencer AK, Lau HA, Nelson MW, Giuliano JD, Zabinski JW, Boussios C, Curhan G, Gliklich RE, Warnock DG. A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm. Orphanet J Rare Dis 2021;16:518. [PMID: 34930374 DOI: 10.1186/s13023-021-02150-3] [Reference Citation Analysis]
52 Paranjape K, Schinkel M, Hammer RD, Schouten B, Nannan Panday RS, Elbers PWG, Kramer MHH, Nanayakkara P. The Value of Artificial Intelligence in Laboratory Medicine. Am J Clin Pathol 2021;155:823-31. [PMID: 33313667 DOI: 10.1093/ajcp/aqaa170] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
53 Radhakrishna U, Vishweswaraiah S, Uppala LV, Szymanska M, Macknis J, Kumar S, Saleem-Rasheed F, Aydas B, Forray A, Muvvala SB, Mishra NK, Guda C, Carey DJ, Metpally RP, Crist RC, Berrettini WH, Bahado-Singh RO. Placental DNA methylation profiles in opioid-exposed pregnancies and associations with the neonatal opioid withdrawal syndrome. Genomics 2021;113:1127-35. [PMID: 33711455 DOI: 10.1016/j.ygeno.2021.03.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
54 Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021;13:152. [PMID: 34579788 DOI: 10.1186/s13073-021-00968-x] [Reference Citation Analysis]
55 López-López E, Bajorath J, Medina-Franco JL. Informatics for Chemistry, Biology, and Biomedical Sciences. J Chem Inf Model 2021;61:26-35. [PMID: 33382611 DOI: 10.1021/acs.jcim.0c01301] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 6.5] [Reference Citation Analysis]
56 Liu Z, Roberts RA, Lal-Nag M, Chen X, Huang R, Tong W. AI-based language models powering drug discovery and development. Drug Discov Today 2021:S1359-6446(21)00281-6. [PMID: 34216835 DOI: 10.1016/j.drudis.2021.06.009] [Reference Citation Analysis]
57 Tremblay J, Haloui M, Attaoua R, Tahir R, Hishmih C, Harvey F, Marois-Blanchet FC, Long C, Simon P, Santucci L, Hizel C, Chalmers J, Marre M, Harrap S, Cífková R, Krajčoviechová A, Matthews DR, Williams B, Poulter N, Zoungas S, Colagiuri S, Mancia G, Grobbee DE, Rodgers A, Liu L, Agbessi M, Bruat V, Favé MJ, Harwood MP, Awadalla P, Woodward M, Hussin JG, Hamet P. Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control. Diabetologia 2021;64:2012-25. [PMID: 34226943 DOI: 10.1007/s00125-021-05491-7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]