Arnedo-Pac C, Mularoni L, Muinos F, Gonzalez-Perez A, Lopez-Bigas N (2019) OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers. Bioinformatics 35:4788–4790. https://doi.org/10.1093/bioinformatics/btz501
Article CAS PubMed PubMed Central Google Scholar
Cancer Genome Atlas Research Network. Electronic address, w. b. e. & Cancer Genome Atlas Research, N (2017) Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 169(1327–1341):e1323. https://doi.org/10.1016/j.cell.2017.05.046
Carithers LJ, Moore HM (2015) The genotype-tissue expression (GTEx) project. Biopreserv Biobank 13:307–308. https://doi.org/10.1089/bio.2015.29031.hmm
Article PubMed PubMed Central Google Scholar
Chakravarty D et al (2017) OncoKB: a precision oncology knowledge base. JCO Precis Oncol. https://doi.org/10.1200/PO.17.00011
Article PubMed PubMed Central Google Scholar
Chen B et al (2017) AHNAK suppresses tumour proliferation and invasion by targeting multiple pathways in triple-negative breast cancer. J Exp Clin Cancer Res 36:65. https://doi.org/10.1186/s13046-017-0522-4
Article CAS PubMed PubMed Central Google Scholar
Cheng F et al (2014) Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome. Mol Biol Evol 31:2156–2169. https://doi.org/10.1093/molbev/msu167
Article CAS PubMed PubMed Central Google Scholar
Cheng F, Zhao J, Zhao Z (2016) Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes. Brief Bioinform 17:642–656. https://doi.org/10.1093/bib/bbv068
Article CAS PubMed Google Scholar
Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. https://doi.org/10.1186/s12864-019-6413-7
Cowley GS et al (2014) Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci Data 1:140035. https://doi.org/10.1038/sdata.2014.35
Article CAS PubMed PubMed Central Google Scholar
Flanagan SE, Patch AM, Ellard S (2010) Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations. Genet Test Mol Biomark 14:533–537. https://doi.org/10.1089/gtmb.2010.0036
Gumpinger AC, Lage K, Horn H, Borgwardt K (2020) Prediction of cancer driver genes through network-based moment propagation of mutation scores. Bioinformatics 36:i508–i515. https://doi.org/10.1093/bioinformatics/btaa452
Article CAS PubMed PubMed Central Google Scholar
Hassan MK, Kumar D, Naik M, Dixit M (2018) The expression profile and prognostic significance of eukaryotic translation elongation factors in different cancers. PLoS ONE 13:e0191377. https://doi.org/10.1371/journal.pone.0191377
Article CAS PubMed PubMed Central Google Scholar
He D et al (2021a) Prioritization of schizophrenia risk genes from GWAS results by integrating multi-omics data. Transl Psychiatry 11:175. https://doi.org/10.1038/s41398-021-01294-x
Article CAS PubMed PubMed Central Google Scholar
He D et al (2024) Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework. EBioMedicine 107:105286. https://doi.org/10.1016/j.ebiom.2024.105286
Article CAS PubMed PubMed Central Google Scholar
He D, Fan C, Qi M, Yang Y, Cooper DN (2021b) Prioritization of schizophrenia risk genes from GWAS results by integrating. Multi-Omics Data 11:175. https://doi.org/10.1038/s41398-021-01294-x
He X, Cai D, Niyogi P (2005) In Proceedings of the 18th international conference on neural information processing systems, pp 507–514, MIT Press, Vancouver
Howe KL et al (2021) Ensembl 2021. Nucleic Acids Res 49:D884-d891. https://doi.org/10.1093/nar/gkaa942
Article CAS PubMed Google Scholar
Kandoth C et al (2013) Mutational landscape and significance across 12 major cancer types. Nature 502:333–339. https://doi.org/10.1038/nature12634
Article CAS PubMed PubMed Central Google Scholar
Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30. https://doi.org/10.1093/nar/28.1.27
Article CAS PubMed PubMed Central Google Scholar
Kobayashi Y, Yonehara S (2009) Novel cell death by downregulation of eEF1A1 expression in tetraploids. Cell Death Differ 16:139–150. https://doi.org/10.1038/cdd.2008.136
Article CAS PubMed Google Scholar
Kut C, Kleinberg L (2023) Radiotherapy, lymphopenia and improving the outcome for glioblastoma: a narrative review. Chin Clin Oncol 12:4. https://doi.org/10.21037/cco-22-94
Lawrence MS et al (2013) Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499:214–218. https://doi.org/10.1038/nature12213
Article CAS PubMed PubMed Central Google Scholar
Lee SY (2016) Temozolomide resistance in glioblastoma multiforme. Genes Dis 3:198–210. https://doi.org/10.1016/j.gendis.2016.04.007
Article PubMed PubMed Central Google Scholar
Li B et al (2009) Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics (Oxford, England) 25:2744–2750. https://doi.org/10.1093/bioinformatics/btp528
Article CAS PubMed Google Scholar
Liu X, Wu C, Li C, Boerwinkle E (2016) dbNSFP v3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs. Human Mutat 37:235–241. https://doi.org/10.1002/humu.22932
Luna A et al (2021) Cell Miner cross-database (CellMinerCDB) version 1.2: exploration of patient-derived cancer cell line pharmacogenomics. Nucl Acids Res 49:D1083–D1093. https://doi.org/10.1093/nar/gkaa968
Article CAS PubMed Google Scholar
Martinez-Jimenez F et al (2020) A compendium of mutational cancer driver genes. Nat Rev Cancer 20:555–572. https://doi.org/10.1038/s41568-020-0290-x
Article CAS PubMed Google Scholar
Mularoni L, Sabarinathan R, Deu-Pons J, Gonzalez-Perez A, Lopez-Bigas N (2016) OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations. Genome Biol 17:128. https://doi.org/10.1186/s13059-016-0994-0
Article CAS PubMed PubMed Central Google Scholar
Oughtred R et al (2021) The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci 30:187–200. https://doi.org/10.1002/pro.3978
Article CAS PubMed Google Scholar
Pacini C et al (2024) A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization. Cancer Cell 42:301-316.e309. https://doi.org/10.1016/j.ccell.2023.12.016
Comments (0)