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For: Andreoletti G, Pal LR, Moult J, Brenner SE. Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation. Hum Mutat 2019;40:1197-201. [PMID: 31334884 DOI: 10.1002/humu.23876] [Cited by in Crossref: 24] [Cited by in F6Publishing: 24] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 Liu Y, Yeung WSB, Chiu PCN, Cao D. Computational approaches for predicting variant impact: An overview from resources, principles to applications. Front Genet 2022;13:981005. [DOI: 10.3389/fgene.2022.981005] [Reference Citation Analysis]
2 Dick K, Kyrollos DG, Cosoreanu ED, Dooley J, Fryer JS, Gordon SM, Kharbanda N, Klamrowski M, LaCasse PNL, Leung TF, Nasir MA, Qiu C, Robinson AS, Shao D, Siromahov BR, Starlight E, Tran C, Wang C, Yang YK, Green JR. Reciprocal perspective as a super learner improves drug-target interaction prediction (MUSDTI). Sci Rep 2022;12:13237. [PMID: 35918366 DOI: 10.1038/s41598-022-16493-9] [Reference Citation Analysis]
3 Livesey BJ, Marsh JA. Interpreting protein variant effects with computational predictors and deep mutational scanning. Dis Model Mech 2022;15:dmm049510. [PMID: 35736673 DOI: 10.1242/dmm.049510] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Olson ND, Wagner J, McDaniel J, Stephens SH, Westreich ST, Prasanna AG, Johanson E, Boja E, Maier EJ, Serang O, Jáspez D, Lorenzo-Salazar JM, Muñoz-Barrera A, Rubio-Rodríguez LA, Flores C, Kyriakidis K, Malousi A, Shafin K, Pesout T, Jain M, Paten B, Chang PC, Kolesnikov A, Nattestad M, Baid G, Goel S, Yang H, Carroll A, Eveleigh R, Bourgey M, Bourque G, Li G, Ma C, Tang L, Du Y, Zhang S, Morata J, Tonda R, Parra G, Trotta JR, Brueffer C, Demirkaya-Budak S, Kabakci-Zorlu D, Turgut D, Kalay Ö, Budak G, Narcı K, Arslan E, Brown R, Johnson IJ, Dolgoborodov A, Semenyuk V, Jain A, Tetikol HS, Jain V, Ruehle M, Lajoie B, Roddey C, Catreux S, Mehio R, Ahsan MU, Liu Q, Wang K, Sahraeian SME, Fang LT, Mohiyuddin M, Hung C, Jain C, Feng H, Li Z, Chen L, Sedlazeck FJ, Zook JM. PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions. Cell Genom 2022;2:100129. [PMID: 35720974 DOI: 10.1016/j.xgen.2022.100129] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
5 Dlamini Z, Skepu A, Kim N, Mkhabele M, Khanyile R, Molefi T, Mbatha S, Setlai B, Mulaudzi T, Mabongo M, Bida M, Kgoebane-maseko M, Mathabe K, Lockhat Z, Kgokolo M, Chauke-malinga N, Ramagaga S, Hull R. AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care. Informatics in Medicine Unlocked 2022. [DOI: 10.1016/j.imu.2022.100965] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Nicora G, Zucca S, Limongelli I, Bellazzi R, Magni P. A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization. Sci Rep 2022;12:2517. [PMID: 35169226 DOI: 10.1038/s41598-022-06547-3] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
7 Livesey BJ, Marsh JA. The properties of human disease mutations at protein interfaces. PLoS Comput Biol 2022;18:e1009858. [PMID: 35120134 DOI: 10.1371/journal.pcbi.1009858] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
8 Mokhtari DA, Appel MJ, Fordyce PM, Herschlag D. High throughput and quantitative enzymology in the genomic era. Curr Opin Struct Biol 2021;71:259-73. [PMID: 34592682 DOI: 10.1016/j.sbi.2021.07.010] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
9 Tripathi S, Dsouza NR, Urrutia R, Zimmermann MT. Structural bioinformatics enhances mechanistic interpretation of genomic variation, demonstrated through the analyses of 935 distinct RAS family mutations. Bioinformatics 2021;37:1367-75. [PMID: 33226070 DOI: 10.1093/bioinformatics/btaa972] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
10 Ancien F, Pucci F, Vranken W, Rooman M. MutaFrame - an interpretative visualization framework for deleteriousness prediction of missense variants in the human exome. Bioinformatics 2021:btab453. [PMID: 34165491 DOI: 10.1093/bioinformatics/btab453] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 Tarca AL, Pataki BÁ, Romero R, Sirota M, Guan Y, Kutum R, Gomez-Lopez N, Done B, Bhatti G, Yu T, Andreoletti G, Chaiworapongsa T, Hassan SS, Hsu CD, Aghaeepour N, Stolovitzky G, Csabai I, Costello JC; DREAM Preterm Birth Prediction Challenge Consortium. Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth. Cell Rep Med 2021;2:100323. [PMID: 34195686 DOI: 10.1016/j.xcrm.2021.100323] [Cited by in F6Publishing: 6] [Reference Citation Analysis]
12 Pancotti C, Benevenuta S, Repetto V, Birolo G, Capriotti E, Sanavia T, Fariselli P. A Deep-Learning Sequence-Based Method to Predict Protein Stability Changes Upon Genetic Variations. Genes (Basel) 2021;12:911. [PMID: 34204764 DOI: 10.3390/genes12060911] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
13 Özkan S, Padilla N, de la Cruz X. Towards a New, Endophenotype-Based Strategy for Pathogenicity Prediction in BRCA1 and BRCA2: In Silico Modeling of the Outcome of HDR/SGE Assays for Missense Variants. Int J Mol Sci 2021;22:6226. [PMID: 34207612 DOI: 10.3390/ijms22126226] [Reference Citation Analysis]
14 Petrosino M, Novak L, Pasquo A, Chiaraluce R, Turina P, Capriotti E, Consalvi V. Analysis and Interpretation of the Impact of Missense Variants in Cancer. Int J Mol Sci 2021;22:5416. [PMID: 34063805 DOI: 10.3390/ijms22115416] [Cited by in F6Publishing: 6] [Reference Citation Analysis]
15 Meyer F, Lesker TR, Koslicki D, Fritz A, Gurevich A, Darling AE, Sczyrba A, Bremges A, McHardy AC. Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit. Nat Protoc 2021;16:1785-801. [PMID: 33649565 DOI: 10.1038/s41596-020-00480-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
16 Xi W, Beer MA. Loop competition and extrusion model predicts CTCF interaction specificity. Nat Commun 2021;12:1046. [PMID: 33594051 DOI: 10.1038/s41467-021-21368-0] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
17 Brini E, Simmerling C, Dill K. Protein storytelling through physics. Science 2020;370:eaaz3041. [PMID: 33243857 DOI: 10.1126/science.aaz3041] [Cited by in Crossref: 21] [Cited by in F6Publishing: 22] [Article Influence: 21.0] [Reference Citation Analysis]
18 Strokach A, Lu TY, Kim PM. ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations. J Mol Biol 2021;433:166810. [PMID: 33450251 DOI: 10.1016/j.jmb.2021.166810] [Cited by in Crossref: 1] [Cited by in F6Publishing: 7] [Article Influence: 1.0] [Reference Citation Analysis]
19 Louie W, Shen MW, Tahiry Z, Zhang S, Worstell D, Cassa CA, Sherwood RI, Gifford DK. Machine learning based CRISPR gRNA design for therapeutic exon skipping. PLoS Comput Biol 2021;17:e1008605. [PMID: 33417623 DOI: 10.1371/journal.pcbi.1008605] [Reference Citation Analysis]
20 Ponzoni L, Peñaherrera DA, Oltvai ZN, Bahar I. Rhapsody: predicting the pathogenicity of human missense variants. Bioinformatics 2020;36:3084-92. [PMID: 32101277 DOI: 10.1093/bioinformatics/btaa127] [Cited by in Crossref: 23] [Cited by in F6Publishing: 24] [Article Influence: 11.5] [Reference Citation Analysis]
21 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: 87] [Article Influence: 23.0] [Reference Citation Analysis]
22 Pey AL. Towards Accurate Genotype-Phenotype Correlations in the CYP2D6 Gene. J Pers Med 2020;10:E158. [PMID: 33049937 DOI: 10.3390/jpm10040158] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
23 Sanavia T, Birolo G, Montanucci L, Turina P, Capriotti E, Fariselli P. Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine. Comput Struct Biotechnol J 2020;18:1968-79. [PMID: 32774791 DOI: 10.1016/j.csbj.2020.07.011] [Cited by in Crossref: 15] [Cited by in F6Publishing: 32] [Article Influence: 7.5] [Reference Citation Analysis]
24 Fenton AW, Page BM, Spellman-Kruse A, Hagenbuch B, Swint-Kruse L. Rheostat positions: A new classification of protein positions relevant to pharmacogenomics. Med Chem Res 2020;29:1133-46. [PMID: 32641900 DOI: 10.1007/s00044-020-02582-9] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
25 Hu Z, Yu C, Furutsuki M, Andreoletti G, Ly M, Hoskins R, Adhikari AN, Brenner SE. VIPdb, a genetic Variant Impact Predictor Database. Hum Mutat 2019;40:1202-14. [PMID: 31283070 DOI: 10.1002/humu.23858] [Cited by in Crossref: 13] [Cited by in F6Publishing: 14] [Article Influence: 4.3] [Reference Citation Analysis]