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For: Shameer K, Tripathi LP, Kalari KR, Dudley JT, Sowdhamini R. Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment. Brief Bioinform 2016;17:841-62. [PMID: 26494363 DOI: 10.1093/bib/bbv084] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 1.9] [Reference Citation Analysis]
Number Citing Articles
1 Shameer K, Nayarisseri A, Romero Duran FX, Gonzalez-Diaz H. Editorial: Improving Neuropharmacology using Big Data, Machine Learning and Computational Algorithms. Curr Neuropharmacol 2017;15:1058-61. [PMID: 29199918 DOI: 10.2174/1570159X1508171114113425] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
2 Müller H, Jimenez-Heredia R, Krolo A, Hirschmugl T, Dmytrus J, Boztug K, Bock C. VCF.Filter: interactive prioritization of disease-linked genetic variants from sequencing data. Nucleic Acids Res 2017;45:W567-72. [PMID: 28520890 DOI: 10.1093/nar/gkx425] [Cited by in Crossref: 17] [Cited by in F6Publishing: 10] [Article Influence: 5.7] [Reference Citation Analysis]
3 Cheng SJ, Shi FY, Liu H, Ding Y, Jiang S, Liang N, Gao G. Accurately annotate compound effects of genetic variants using a context-sensitive framework. Nucleic Acids Res 2017;45:e82. [PMID: 28158838 DOI: 10.1093/nar/gkx041] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 1.6] [Reference Citation Analysis]
4 Ariza MJ, Pérez-López C, Almagro F, Sánchez-Tévar AM, Muñiz-Grijalvo O, Álvarez-Sala Walter LA, Rioja J, Sánchez-Chaparro MÁ, Valdivielso P. Genetic variants in the LPL and GPIHBP1 genes, in patients with severe hypertriglyceridaemia, detected with high resolution melting analysis. Clin Chim Acta 2020;500:163-71. [PMID: 31669931 DOI: 10.1016/j.cca.2019.10.011] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 0.7] [Reference Citation Analysis]
5 Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: are we there yet? Heart 2018;104:1156-64. [PMID: 29352006 DOI: 10.1136/heartjnl-2017-311198] [Cited by in Crossref: 158] [Cited by in F6Publishing: 125] [Article Influence: 39.5] [Reference Citation Analysis]
6 Petrini A, Mesiti M, Schubach M, Frasca M, Danis D, Re M, Grossi G, Cappelletti L, Castrignanò T, Robinson PN, Valentini G. parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants. Gigascience 2020;9:giaa052. [PMID: 32444882 DOI: 10.1093/gigascience/giaa052] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
7 Li W, Duren Z, Jiang R, Wong WH. A method for scoring the cell type-specific impacts of noncoding variants in personal genomes. Proc Natl Acad Sci U S A 2020;117:21364-72. [PMID: 32817564 DOI: 10.1073/pnas.1922703117] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
8 International Society for Biocuration. Biocuration: Distilling data into knowledge. PLoS Biol 2018;16:e2002846. [PMID: 29659566 DOI: 10.1371/journal.pbio.2002846] [Cited by in Crossref: 35] [Cited by in F6Publishing: 25] [Article Influence: 8.8] [Reference Citation Analysis]
9 Uversky VN. Intrinsically disordered proteins in overcrowded milieu: Membrane-less organelles, phase separation, and intrinsic disorder. Curr Opin Struct Biol 2017;44:18-30. [PMID: 27838525 DOI: 10.1016/j.sbi.2016.10.015] [Cited by in Crossref: 293] [Cited by in F6Publishing: 258] [Article Influence: 48.8] [Reference Citation Analysis]
10 van Ooijen MP, Jong VL, Eijkemans MJC, Heck AJR, Andeweg AC, Binai NA, van den Ham H. Identification of differentially expressed peptides in high-throughput proteomics data. Briefings in Bioinformatics 2018;19:971-81. [DOI: 10.1093/bib/bbx031] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 2.2] [Reference Citation Analysis]