{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T09:50:22Z","timestamp":1775382622147,"version":"3.50.1"},"reference-count":156,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009910","name":"Hartmanns Fond","doi-asserted-by":"crossref","award":["R241-A33877"],"award-info":[{"award-number":["R241-A33877"]}],"id":[{"id":"10.13039\/501100009910","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012331","name":"LEO Fondet","doi-asserted-by":"publisher","award":["LF17006"],"award-info":[{"award-number":["LF17006"]}],"id":[{"id":"10.13039\/501100012331","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Carlsberg Foundation Distinguished Fellowship","award":["CF18-0314"],"award-info":[{"award-number":["CF18-0314"]}]},{"name":"NovoNordisk Fonden Bioscience and Basic Biomedicine","award":["NNF20OC0065262"],"award-info":[{"award-number":["NNF20OC0065262"]}]},{"name":"Center of Excellence in Autophagy, Recycling and Disease"},{"DOI":"10.13039\/501100001732","name":"Danish National Research Foundation","doi-asserted-by":"publisher","award":["DNRF-125"],"award-info":[{"award-number":["DNRF-125"]}],"id":[{"id":"10.13039\/501100001732","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.<\/jats:p>","DOI":"10.1093\/bib\/bbad519","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T15:10:07Z","timestamp":1706022607000},"source":"Crossref","is-referenced-by-count":25,"title":["Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks"],"prefix":"10.1093","volume":"25","author":[{"given":"Mona","family":"Nourbakhsh","sequence":"first","affiliation":[{"name":"Cancer Systems Biology , Section for Bioinformatics, Department of Health Technology, , 2800 Lyngby , Denmark"},{"name":"Technical University of Denmark , Section for Bioinformatics, Department of Health Technology, , 2800 Lyngby , Denmark"}]},{"given":"Kristine","family":"Degn","sequence":"additional","affiliation":[{"name":"Cancer Systems Biology , Section for Bioinformatics, Department of Health Technology, , 2800 Lyngby , Denmark"},{"name":"Technical University of Denmark , Section for Bioinformatics, Department of Health Technology, , 2800 Lyngby , Denmark"}]},{"given":"Astrid","family":"Saksager","sequence":"additional","affiliation":[{"name":"Cancer Systems Biology , Section for Bioinformatics, Department of Health Technology, , 2800 Lyngby , Denmark"},{"name":"Technical University of Denmark , Section for Bioinformatics, Department of Health Technology, , 2800 Lyngby , Denmark"}]},{"given":"Matteo","family":"Tiberti","sequence":"additional","affiliation":[{"name":"Cancer Structural Biology, Danish Cancer Institute , 2100 Copenhagen , Denmark"}]},{"given":"Elena","family":"Papaleo","sequence":"additional","affiliation":[{"name":"Cancer Systems Biology , Section for Bioinformatics, Department of Health Technology, , 2800 Lyngby , Denmark"},{"name":"Technical University of Denmark , Section for Bioinformatics, Department of Health Technology, , 2800 Lyngby , Denmark"},{"name":"Cancer Structural Biology, Danish Cancer Institute , 2100 Copenhagen , Denmark"}]}],"member":"286","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"2024012315095442700_ref1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1158\/2159-8290.CD-21-1059","article-title":"Hallmarks of cancer: new dimensions","volume":"12","author":"Hanahan","year":"2022","journal-title":"Cancer Discov"},{"key":"2024012315095442700_ref2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0092-8674(00)81683-9","article-title":"The hallmarks of cancer review evolve progressively from normalcy via a series of pre","volume":"100","author":"Hanahan","year":"2000","journal-title":"Cell"},{"key":"2024012315095442700_ref3","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.cell.2011.02.013","article-title":"Hallmarks of cancer: the next generation","volume":"144","author":"Hanahan","year":"2011","journal-title":"Cell"},{"key":"2024012315095442700_ref4","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1038\/nrc795","article-title":"Modelling the molecular circuitry of cancer","volume":"2","author":"Hahn","year":"2002","journal-title":"Nat Rev Cancer"},{"issue":"339","key":"2024012315095442700_ref5","first-page":"1546","article-title":"Cancer genome landscapes","volume":"339","author":"Vogelstein","year":"1979","journal-title":"Science"},{"key":"2024012315095442700_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-72404-w","article-title":"Identification of pathogenic missense mutations using protein stability predictors","volume":"10","author":"Gerasimavicius","year":"2020","journal-title":"Sci Rep"},{"key":"2024012315095442700_ref7","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.tibs.2019.01.003","article-title":"Biophysical and mechanistic models for disease-causing protein variants","volume":"44","author":"Stein","year":"2019","journal-title":"Trends Biochem Sci"},{"key":"2024012315095442700_ref8","doi-asserted-by":"crossref","first-page":"e4527","DOI":"10.1002\/pro.4527","article-title":"RosettaDDGPrediction for high-throughput mutational scans: from stability to binding","volume":"32","author":"Sora","year":"2023","journal-title":"Protein Sci"},{"key":"2024012315095442700_ref9","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.1080\/15548627.2020.1847443","article-title":"The conformational and mutational landscape of the ubiquitin-like marker for autophagosome formation in cancer","volume":"17","author":"Fas","year":"2021","journal-title":"Autophagy"},{"issue":"3","key":"2024012315095442700_ref10","doi-asserted-by":"crossref","first-page":"30","DOI":"10.3390\/jpm8030030","article-title":"The role of next-generation sequencing in precision medicine: a review of outcomes in oncology","volume":"8","author":"Morash","year":"2018","journal-title":"J Pers Med"},{"key":"2024012315095442700_ref11","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1038\/nrg.2017.44","article-title":"Reference standards for next-generation sequencing","volume":"18","author":"Hardwick","year":"2017","journal-title":"Nat Rev Genet"},{"key":"2024012315095442700_ref12","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.molmed.2023.03.007","article-title":"Cancer driver mutations: predictions and reality","volume":"29","author":"Ostroverkhova","year":"2023","journal-title":"Trends Mol Med"},{"key":"2024012315095442700_ref13","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa303","article-title":"Recent advances in network-based methods for disease gene prediction","volume":"22","author":"Ata","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024012315095442700_ref14","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.3390\/ijms19072113","article-title":"Computational approaches to prioritize cancer driver missense mutations","volume":"19","author":"Zhao","year":"2018","journal-title":"Int J Mol Sci"},{"key":"2024012315095442700_ref15","doi-asserted-by":"crossref","DOI":"10.1016\/j.sbi.2023.102600","article-title":"Protein structure-based evaluation of missense variants: resources, challenges and future directions","volume":"80","author":"David","year":"2023","journal-title":"Curr Opin Struct Biol"},{"key":"2024012315095442700_ref16","doi-asserted-by":"crossref","first-page":"105695","DOI":"10.1016\/j.compbiomed.2022.105695","article-title":"Protein structural bioinformatics: an overview","volume":"147","author":"Paiva","year":"2022","journal-title":"Comput Biol Med"},{"key":"2024012315095442700_ref17","doi-asserted-by":"crossref","first-page":"bbaa250","DOI":"10.1093\/bib\/bbaa250","article-title":"Prediction of driver variants in the cancer genome via machine learning methodologies","volume":"22","author":"Rogers","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024012315095442700_ref18","article-title":"Predicting mutational function using machine learning","volume":"791","author":"Shea","year":"2023","journal-title":"Mutat Res\/Rev Mutat Res"},{"key":"2024012315095442700_ref19","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/gm390","article-title":"Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation","volume":"4","author":"Gonzalez-Perez","year":"2012","journal-title":"Genome Med"},{"key":"2024012315095442700_ref20","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1038\/s41597-019-0096-4","article-title":"Barriers to accessing public cancer genomic data","volume":"6","author":"Learned","year":"2019","journal-title":"Sci Data"},{"key":"2024012315095442700_ref21","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cels.2019.05.005","article-title":"CHASMplus reveals the scope of somatic missense mutations driving human cancers","volume":"9","author":"Tokheim","year":"2019","journal-title":"Cell Syst"},{"key":"2024012315095442700_ref22","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13073-021-00835-9","article-title":"CADD-Splice\u2014improving genome-wide variant effect prediction using deep learning-derived splice scores","volume":"13","author":"Rentzsch","year":"2021","journal-title":"Genome Med"},{"key":"2024012315095442700_ref23","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1093\/bioinformatics\/btv009","article-title":"An integrative approach to predicting the functional effects of non-coding and coding sequence variation","volume":"31","author":"Shihab","year":"2015","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref24","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1038\/s41586-021-03771-1","article-title":"In silico saturation mutagenesis of cancer genes","volume":"596","author":"Mui\u00f1os","year":"2021","journal-title":"Nature"},{"key":"2024012315095442700_ref25","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/nature12912","article-title":"Discovery and saturation analysis of cancer genes across 21 tumour types","volume":"505","author":"Lawrence","year":"2014","journal-title":"Nature"},{"key":"2024012315095442700_ref26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbac062","article-title":"Machine learning methods for prediction of cancer driver genes: a survey paper","volume":"23","author":"Andrades","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024012315095442700_ref27","doi-asserted-by":"crossref","first-page":"2823","DOI":"10.1002\/1878-0261.13056","article-title":"Global mapping of cancers: the cancer genome atlas and beyond","volume":"15","author":"Ganini","year":"2021","journal-title":"Mol Oncol"},{"key":"2024012315095442700_ref28","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/s41587-019-0055-9","article-title":"The International Cancer Genome Consortium data portal","volume":"37","author":"Zhang","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2024012315095442700_ref29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1200\/PO.17.00011","article-title":"OncoKB: a precision oncology Knowledge Base","volume":"2017","author":"Chakravarty","year":"2017","journal-title":"JCO Precis Oncol"},{"key":"2024012315095442700_ref30","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The Protein Data Bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref31","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"2024012315095442700_ref32","doi-asserted-by":"crossref","first-page":"D439","DOI":"10.1093\/nar\/gkab1061","article-title":"AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models","volume":"50","author":"Varadi","year":"2022","journal-title":"Nucleic Acids Res"},{"issue":"379","key":"2024012315095442700_ref33","first-page":"1123","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Lin","year":"1979","journal-title":"Science"},{"key":"2024012315095442700_ref34","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41568-020-0290-x","article-title":"A compendium of mutational cancer driver genes","volume":"20","author":"Mart\u00ednez-Jim\u00e9nez","year":"2020","journal-title":"Nat Rev Cancer"},{"key":"2024012315095442700_ref35","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.cell.2018.02.060","article-title":"Comprehensive characterization of cancer driver genes and mutations","volume":"173","author":"Bailey","year":"2018","journal-title":"Cell"},{"key":"2024012315095442700_ref36","doi-asserted-by":"crossref","first-page":"797","DOI":"10.3390\/genes10100797","article-title":"Biological network approaches and applications in rare disease studies","volume":"10","author":"Zhang","year":"2019","journal-title":"Genes (Basel)"},{"key":"2024012315095442700_ref37","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007538","article-title":"CBNA: a control theory based method for identifying coding and non-coding cancer drivers","volume":"15","author":"Pham","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2024012315095442700_ref38","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3389\/fgene.2020.00377","article-title":"Prioritizing cancer genes based on an improved Random Walk method","volume":"11","author":"Wei","year":"2020","journal-title":"Front Genet"},{"key":"2024012315095442700_ref39","doi-asserted-by":"crossref","first-page":"103661","DOI":"10.1016\/j.jbi.2020.103661","article-title":"GenHITS: a network science approach to driver gene detection in human regulatory network using gene\u2019s influence evaluation","volume":"114","author":"Akhavan-Safar","year":"2021","journal-title":"J Biomed Inform"},{"key":"2024012315095442700_ref40","doi-asserted-by":"crossref","first-page":"104326","DOI":"10.1016\/j.biosystems.2020.104326","article-title":"KatzDriver: a network based method to cancer causal genes discovery in gene regulatory network","volume":"201","author":"Akhavan-Safar","year":"2021","journal-title":"Biosystems"},{"key":"2024012315095442700_ref41","doi-asserted-by":"crossref","first-page":"103362","DOI":"10.1016\/j.compbiomed.2019.103362","article-title":"Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach","volume":"114","author":"Rahimi","year":"2019","journal-title":"Comput Biol Med"},{"key":"2024012315095442700_ref42","doi-asserted-by":"crossref","first-page":"I583","DOI":"10.1093\/bioinformatics\/btaa797","article-title":"DriverGroup: a novel method for identifying driver gene groups","volume":"36","author":"Pham","year":"2020","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref43","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-021-00371-z","article-title":"Non-coding driver mutations in human cancer","volume":"21","author":"Elliott","year":"2021","journal-title":"Nat Rev Cancer"},{"key":"2024012315095442700_ref44","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.ebiom.2017.11.028","article-title":"Module analysis captures pancancer genetically and epigenetically deregulated cancer driver genes for smoking and antiviral response","volume":"27","author":"Champion","year":"2018","journal-title":"EBioMedicine"},{"key":"2024012315095442700_ref45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/4826206","article-title":"DriverFinder: a gene length-based network method to identify cancer driver genes","volume":"2017","author":"Wei","year":"2017","journal-title":"Complexity"},{"key":"2024012315095442700_ref46","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.1093\/bioinformatics\/btz815","article-title":"PRODIGY: personalized prioritization of driver genes","volume":"36","author":"Dinstag","year":"2020","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref47","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1186\/s12859-016-1332-y","article-title":"LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network","volume":"17","author":"Wei","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2024012315095442700_ref48","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gku1393","article-title":"Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles","volume":"43","author":"Bertrand","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref49","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s10528-019-09924-2","article-title":"A novel method for identifying the potential cancer driver genes based on molecular data integration","volume":"58","author":"Zhang","year":"2020","journal-title":"Biochem Genet"},{"key":"2024012315095442700_ref50","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3389\/fgene.2020.607798","article-title":"DriverSubNet: a novel algorithm for identifying cancer driver genes by subnetwork enrichment analysis","volume":"11","author":"Zhang","year":"2021","journal-title":"Front Genet"},{"key":"2024012315095442700_ref51","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1186\/s12859-019-2847-9","article-title":"A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph","volume":"20","author":"Song","year":"2019","journal-title":"BMC Bioinform"},{"key":"2024012315095442700_ref52","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1093\/bioinformatics\/btz655","article-title":"MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules","volume":"36","author":"Ahmed","year":"2020","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref53","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ymeth.2020.07.013","article-title":"Identifying and ranking potential cancer drivers using representation learning on attributed network","volume":"192","author":"Peng","year":"2021","journal-title":"Methods"},{"key":"2024012315095442700_ref54","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/TCBB.2020.3020096","article-title":"FrDriver: a functional region driver identification for protein sequence","volume":"18","author":"Lu","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2024012315095442700_ref55","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1158\/2159-8290.CD-20-1334","article-title":"Discovery of candidate DNA methylation cancer driver genes","volume":"11","author":"Pan","year":"2021","journal-title":"Cancer Discov"},{"key":"2024012315095442700_ref56","article-title":"Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis","volume":"9","author":"Li","year":"2019","journal-title":"Sci Rep"},{"key":"2024012315095442700_ref57","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkz096","article-title":"DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies","volume":"47","author":"Han","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref58","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1093\/bib\/bbz089","article-title":"Network control principles for identifying personalized driver genes in cancer","volume":"21","author":"Guo","year":"2020","journal-title":"Brief Bioinform"},{"key":"2024012315095442700_ref59","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gks743","article-title":"Functional impact bias reveals cancer drivers","volume":"40","author":"Gonzalez-Perez","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13073-021-00830-0","article-title":"Pan-cancer detection of driver genes at the single-patient resolution","volume":"13","author":"Nulsen","year":"2021","journal-title":"Genome Med"},{"key":"2024012315095442700_ref61","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1186\/s12859-021-04203-7","article-title":"driveR: a novel method for prioritizing cancer driver genes using somatic genomics data","volume":"22","author":"\u00dclgen","year":"2021","journal-title":"BMC Bioinform"},{"key":"2024012315095442700_ref62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbab432","article-title":"Improving cancer driver gene identification using multi-task learning on graph convolutional network","volume":"23","author":"Peng","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024012315095442700_ref63","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3389\/fgene.2020.564839","article-title":"FI-net: identification of cancer driver genes by using functional impact prediction neural network","volume":"11","author":"Gu","year":"2020","journal-title":"Front Genet"},{"key":"2024012315095442700_ref64","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3389\/fgene.2019.00013","article-title":"DeepDriver: predicting cancer driver genes based on somatic mutations using deep convolutional neural networks","volume":"10","author":"Luo","year":"2019","journal-title":"Front Genet"},{"key":"2024012315095442700_ref65","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1186\/s12859-021-04400-4","article-title":"Deep learning for cancer type classification and driver gene identification","volume":"22","author":"Zeng","year":"2021","journal-title":"BMC Bioinform"},{"key":"2024012315095442700_ref66","doi-asserted-by":"crossref","first-page":"I508","DOI":"10.1093\/bioinformatics\/btaa452","article-title":"Prediction of cancer driver genes through network-based moment propagation of mutation scores","volume":"36","author":"Gumpinger","year":"2020","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref67","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007381","article-title":"LOTUS: a single- and multitask machine learning algorithm for the prediction of cancer driver genes","volume":"15","author":"Collier","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2024012315095442700_ref68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbab548","article-title":"Comprehensive evaluation of computational methods for predicting cancer driver genes","volume":"23","author":"Shi","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024012315095442700_ref69","doi-asserted-by":"crossref","first-page":"4788","DOI":"10.1093\/bioinformatics\/btz501","article-title":"OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers","volume":"35","author":"Arnedo-Pac","year":"2019","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref70","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1016\/j.molcel.2019.12.027","article-title":"Candidate cancer driver mutations in distal regulatory elements and long-range chromatin interaction networks","volume":"77","author":"Zhu","year":"2020","journal-title":"Mol Cell"},{"key":"2024012315095442700_ref71","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1093\/bioinformatics\/btu858","article-title":"MADGiC: a model-based approach for identifying driver genes in cancer","volume":"31","author":"Korthauer","year":"2015","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref72","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1186\/s12859-020-3449-2","article-title":"QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency","volume":"21","author":"Bokhari","year":"2020","journal-title":"BMC Bioinform"},{"key":"2024012315095442700_ref73","doi-asserted-by":"crossref","DOI":"10.1002\/advs.201800640","article-title":"MaxMIF: a new method for identifying cancer driver genes through effective data integration","volume":"5","author":"Hou","year":"2018","journal-title":"Adv Sci"},{"key":"2024012315095442700_ref74","doi-asserted-by":"crossref","first-page":"13124","DOI":"10.1038\/s41598-017-12888-1","article-title":"Signatures of positive selection reveal a universal role of chromatin modifiers as cancer driver genes","volume":"7","author":"Zapata","year":"2017","journal-title":"Sci Rep"},{"key":"2024012315095442700_ref75","first-page":"1","article-title":"Tumor suppressors having oncogenic functions: the double agents","volume":"10","author":"Datta","year":"2020","journal-title":"Cell"},{"key":"2024012315095442700_ref76","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.gene.2013.07.047","article-title":"Antagonistic functional duality of cancer genes","volume":"529","author":"Stepanenko","year":"2013","journal-title":"Gene"},{"key":"2024012315095442700_ref77","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1056\/NEJMra072367","article-title":"Oncogenes and cancer","volume":"358","author":"Croce","year":"2008","journal-title":"N Engl J Med"},{"key":"2024012315095442700_ref78","doi-asserted-by":"crossref","first-page":"2647","DOI":"10.1159\/000495956","article-title":"Loss of tumor suppressor gene function in human cancer: an overview","volume":"51","author":"Wang","year":"2018","journal-title":"Cell Physiol Biochem"},{"key":"2024012315095442700_ref79","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/s41389-018-0034-x","article-title":"Double agents: genes with both oncogenic and tumor-suppressor functions","volume":"7","author":"Shen","year":"2018","journal-title":"Oncogenesis"},{"key":"2024012315095442700_ref80","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1093\/bioinformatics\/btz851","article-title":"Somatic selection distinguishes oncogenes and tumor suppressor genes","volume":"36","author":"Chandrashekar","year":"2020","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref81","doi-asserted-by":"crossref","first-page":"14330","DOI":"10.1073\/pnas.1616440113","article-title":"Evaluating the evaluation of cancer driver genes","volume":"113","author":"Tokheim","year":"2016","journal-title":"Proc Natl Acad Sci USA"},{"key":"2024012315095442700_ref82","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.aba6784","article-title":"DORGE: discovery of oncogenes and tumoR suppressor genes using genetic and epigenetic features","volume":"6","author":"Lyu","year":"2020","journal-title":"Sci Adv"},{"key":"2024012315095442700_ref83","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1038\/s41467-019-13803-0","article-title":"Interpreting pathways to discover cancer driver genes with Moonlight","volume":"11","author":"Colaprico","year":"2020","journal-title":"Nat Commun"},{"key":"2024012315095442700_ref84","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad274","article-title":"A workflow to study mechanistic indicators for driver gene prediction with Moonlight","volume":"24","author":"Nourbakhsh","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024012315095442700_ref85","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.cels.2020.06.005","article-title":"PertInInt: an integrative, analytical approach to rapidly uncover cancer driver genes with perturbed interactions and functionalities","volume":"11","author":"Kobren","year":"2020","journal-title":"Cell Syst"},{"key":"2024012315095442700_ref86","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1038\/s41568-018-0060-1","article-title":"The COSMIC cancer gene census: describing genetic dysfunction across all human cancers","volume":"18","author":"Sondka","year":"2018","journal-title":"Nat Rev Cancer"},{"key":"2024012315095442700_ref87","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-018-1612-0","article-title":"The network of cancer genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens","volume":"20","author":"Repana","year":"2019","journal-title":"Genome Biol"},{"key":"2024012315095442700_ref88","doi-asserted-by":"crossref","first-page":"e70","DOI":"10.1093\/nar\/gkac215","article-title":"EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants","volume":"50","author":"Parvandeh","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref89","doi-asserted-by":"crossref","first-page":"13452","DOI":"10.1038\/s41598-019-48765-2","article-title":"Estimating the frequency of single point driver mutations across common solid tumours","volume":"9","author":"Darbyshire","year":"2019","journal-title":"Sci Rep"},{"key":"2024012315095442700_ref90","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1016\/j.cell.2017.09.042","article-title":"Universal patterns of selection in cancer and somatic tissues","volume":"171","author":"Martincorena","year":"2017","journal-title":"Cell"},{"key":"2024012315095442700_ref91","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.1093\/hmg\/ddu733","article-title":"Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies","volume":"24","author":"Dong","year":"2015","journal-title":"Hum Mol Genet"},{"key":"2024012315095442700_ref92","doi-asserted-by":"crossref","first-page":"lqaa084","DOI":"10.1093\/nargab\/lqaa084","article-title":"AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes","volume":"2","author":"Wang","year":"2020","journal-title":"NAR Genom Bioinform"},{"key":"2024012315095442700_ref93","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1001025","article-title":"Identifying a high fraction of the human genome to be under selective constraint using GERP++","volume":"6","author":"Davydov","year":"2010","journal-title":"PLoS Comput Biol"},{"key":"2024012315095442700_ref94","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1038\/ng.3703","article-title":"M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity","volume":"48","author":"Jagadeesh","year":"2016","journal-title":"Nat Genet"},{"key":"2024012315095442700_ref95","doi-asserted-by":"crossref","first-page":"3637","DOI":"10.1093\/bioinformatics\/btaa242","article-title":"CScape-somatic: distinguishing driver and passenger point mutations in the cancer genome","volume":"36","author":"Rogers","year":"2020","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref96","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0077945","article-title":"CanDrA: cancer-specific driver missense mutation annotation with optimized features","volume":"8","author":"Mao","year":"2013","journal-title":"PloS One"},{"key":"2024012315095442700_ref97","doi-asserted-by":"crossref","first-page":"2745","DOI":"10.1093\/bioinformatics\/btv195","article-title":"PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels","volume":"31","author":"Choi","year":"2015","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref98","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1038\/nprot.2009.86","article-title":"Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm","volume":"4","author":"Kumar","year":"2009","journal-title":"Nat Protoc"},{"key":"2024012315095442700_ref99","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/nprot.2015.123","article-title":"SIFT missense predictions for genomes","volume":"11","author":"Vaser","year":"2016","journal-title":"Nat Protoc"},{"key":"2024012315095442700_ref100","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1038\/ng.2892","article-title":"A general framework for estimating the relative pathogenicity of human genetic variants","volume":"46","author":"Kircher","year":"2014","journal-title":"Nat Genet"},{"key":"2024012315095442700_ref101","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1006981","article-title":"Finding driver mutations in cancer: elucidating the role of background mutational processes","volume":"15","author":"Brown","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2024012315095442700_ref102","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1093\/molbev\/msz179","article-title":"GEMME: a simple and fast global Epistatic model predicting mutational effects","volume":"36","author":"Laine","year":"2019","journal-title":"Mol Biol Evol"},{"key":"2024012315095442700_ref103","doi-asserted-by":"crossref","first-page":"5322","DOI":"10.1093\/bioinformatics\/btaa1030","article-title":"DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction","volume":"36","author":"Munro","year":"2020","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref104","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1016\/j.ajhg.2016.08.016","article-title":"REVEL: an ensemble method for predicting the pathogenicity of rare missense variants","volume":"99","author":"Ioannidis","year":"2016","journal-title":"Am J Hum Genet"},{"key":"2024012315095442700_ref105","doi-asserted-by":"crossref","first-page":"7793","DOI":"10.1093\/nar\/gky678","article-title":"Performance evaluation of pathogenicity-computation methods for missense variants","volume":"46","author":"Li","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref106","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1158\/0008-5472.CAN-15-3190","article-title":"Exome-scale discovery of hotspot mutation regions in human cancer using 3D protein structure","volume":"76","author":"Tokheim","year":"2016","journal-title":"Cancer Res"},{"key":"2024012315095442700_ref107","doi-asserted-by":"crossref","first-page":"W201","DOI":"10.1093\/nar\/gkx390","article-title":"DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins","volume":"45","author":"Raimondi","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref108","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1093\/bioinformatics\/btx536","article-title":"FATHMM-XF: accurate prediction of pathogenic point mutations via extended features","volume":"34","author":"Rogers","year":"2018","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref109","doi-asserted-by":"crossref","first-page":"D886","DOI":"10.1093\/nar\/gky1016","article-title":"CADD: predicting the deleteriousness of variants throughout the human genome","volume":"47","author":"Rentzsch","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref110","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbaa119","article-title":"PredCID: prediction of driver frameshift indels in human cancer","volume":"22","author":"Yue","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024012315095442700_ref111","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-11746-4","article-title":"CScape: a tool for predicting oncogenic single-point mutations in the cancer genome","volume":"7","author":"Rogers","year":"2017","journal-title":"Sci Rep"},{"key":"2024012315095442700_ref112","doi-asserted-by":"crossref","first-page":"i389","DOI":"10.1093\/bioinformatics\/btx272","article-title":"When loss-of-function is loss of function: assessing mutational signatures and impact of loss-of-function genetic variants","volume":"33","author":"Pagel","year":"2017","journal-title":"Bioinformatics"},{"key":"2024012315095442700_ref113","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007112","article-title":"Pathogenicity and functional impact of non-frameshifting insertion\/deletion variation in the human genome","volume":"15","author":"Pagel","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2024012315095442700_ref114","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1038\/s41588-018-0167-z","article-title":"Predicting the clinical impact of human mutation with deep neural networks","volume":"50","author":"Sundaram","year":"2018","journal-title":"Nat Genet"},{"key":"2024012315095442700_ref115","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1038\/ng.3477","article-title":"A spectral approach integrating functional genomic annotations for coding and noncoding variants","volume":"48","author":"Ionita-Laza","year":"2016","journal-title":"Nat Genet"},{"key":"2024012315095442700_ref116","doi-asserted-by":"crossref","first-page":"W315","DOI":"10.1093\/nar\/gkz350","article-title":"AlloDriver: a method for the identification and analysis of cancer driver targets","volume":"47","author":"Song","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref117","doi-asserted-by":"crossref","first-page":"10576","DOI":"10.1038\/srep10576","article-title":"A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data","volume":"5","author":"Lu","year":"2015","journal-title":"Sci Rep"},{"key":"2024012315095442700_ref118","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1038\/s41586-021-04043-8","article-title":"Disease variant prediction with deep generative models of evolutionary data","volume":"599","author":"Frazer","year":"2021","journal-title":"Nature"},{"key":"2024012315095442700_ref119","doi-asserted-by":"crossref","first-page":"e9380","DOI":"10.15252\/msb.20199380","article-title":"Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations","volume":"16","author":"Livesey","year":"2020","journal-title":"Mol Syst Biol"},{"key":"2024012315095442700_ref120","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s13059-020-01954-z","article-title":"Comprehensive assessment of computational algorithms in predicting cancer driver mutations","volume":"21","author":"Chen","year":"2020","journal-title":"Genome Biol"},{"key":"2024012315095442700_ref121","doi-asserted-by":"crossref","first-page":"e4","DOI":"10.1093\/nar\/gkab877","article-title":"SBSA: an online service for somatic binding sequence annotation","volume":"50","author":"Jiang","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref122","doi-asserted-by":"crossref","first-page":"5743","DOI":"10.1038\/s41467-021-25976-8","article-title":"ECNet is an evolutionary context-integrated deep learning framework for protein engineering","volume":"12","author":"Luo","year":"2021","journal-title":"Nat Commun"},{"key":"2024012315095442700_ref123","first-page":"12","article-title":"SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering","volume":"15","author":"Li","year":"2023","journal-title":"J Chem"},{"key":"2024012315095442700_ref124","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1186\/s13059-022-02712-z","article-title":"Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions","volume":"23","author":"Kim","year":"2022","journal-title":"Genome Biol"},{"key":"2024012315095442700_ref125","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1038\/s41588-020-00774-y","article-title":"Comprehensive characterization of protein\u2013protein interactions perturbed by disease mutations","volume":"53","author":"Cheng","year":"2021","journal-title":"Nat Genet"},{"key":"2024012315095442700_ref126","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.ccell.2018.01.021","article-title":"Systematic functional annotation of somatic mutations in cancer","volume":"33","author":"Ng","year":"2018","journal-title":"Cancer Cell"},{"key":"2024012315095442700_ref127","doi-asserted-by":"crossref","first-page":"18962","DOI":"10.1073\/pnas.1901156116","article-title":"Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures","volume":"116","author":"Kumar","year":"2019","journal-title":"Proc Natl Acad Sci USA"},{"key":"2024012315095442700_ref128","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1004518","article-title":"A Pan-cancer catalogue of cancer driver protein interaction interfaces","volume":"11","author":"Porta-Pardo","year":"2015","journal-title":"PLoS Comput Biol"},{"key":"2024012315095442700_ref129","article-title":"PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles","volume":"63","author":"Sora","journal-title":"J Chem Inf Model"},{"key":"2024012315095442700_ref130","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1038\/s42256-022-00561-w","article-title":"Predicting functional effect of missense variants using graph attention neural networks","volume":"4","author":"Zhang","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2024012315095442700_ref131","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1002\/humu.22963","article-title":"mutation3D: cancer gene prediction through atomic clustering of coding variants in the structural proteome","volume":"37","author":"Meyer","year":"2016","journal-title":"Hum Mutat"},{"key":"2024012315095442700_ref132","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1038\/ng.3586","article-title":"Protein-structure-guided discovery of functional mutations across 19 cancer types","volume":"48","author":"Niu","year":"2016","journal-title":"Nat Genet"},{"key":"2024012315095442700_ref133","doi-asserted-by":"crossref","first-page":"E5486","DOI":"10.1073\/pnas.1516373112","article-title":"Comprehensive assessment of cancer missense mutation clustering in protein structures","volume":"112","author":"Kamburov","year":"2015","journal-title":"Proc Natl Acad Sci"},{"key":"2024012315095442700_ref134","doi-asserted-by":"crossref","first-page":"D980","DOI":"10.1093\/nar\/gkt1113","article-title":"ClinVar: public archive of relationships among sequence variation and human phenotype","volume":"42","author":"Landrum","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref135","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13073-016-0393-x","article-title":"3D clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets","volume":"9","author":"Gao","year":"2017","journal-title":"Genome Med"},{"key":"2024012315095442700_ref136","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.ajhg.2018.01.017","article-title":"Comprehensive analysis of constraint on the spatial distribution of missense variants in human protein structures","volume":"102","author":"Sivley","year":"2018","journal-title":"Am J Hum Genet"},{"key":"2024012315095442700_ref137","doi-asserted-by":"crossref","first-page":"W463","DOI":"10.1093\/nar\/gkw364","article-title":"StructMAn: annotation of single-nucleotide polymorphisms in the structural context","volume":"44","author":"Gress","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref138","doi-asserted-by":"crossref","first-page":"8960","DOI":"10.1073\/pnas.1820813116","article-title":"Functional characterization of 3D protein structures informed by human genetic diversity","volume":"116","author":"Hicks","year":"2019","journal-title":"Proc Natl Acad Sci"},{"key":"2024012315095442700_ref139","doi-asserted-by":"crossref","first-page":"eadg7492","DOI":"10.1126\/science.adg7492","article-title":"Accurate proteome-wide missense variant effect prediction with AlphaMissense","volume":"381","author":"Cheng","year":"1979","journal-title":"Science"},{"key":"2024012315095442700_ref140","doi-asserted-by":"crossref","first-page":"bbac074","DOI":"10.1093\/bib\/bbac074","article-title":"MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles","volume":"23","author":"Tiberti","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024012315095442700_ref141","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.csbj.2022.11.048","article-title":"Accurate protein stability predictions from homology models","volume":"21","author":"Valanciute","year":"2023","journal-title":"Comput Struct Biotechnol J"},{"key":"2024012315095442700_ref142","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1038\/s41594-022-00849-w","article-title":"A structural biology community assessment of AlphaFold2 applications","volume":"29","author":"Akdel","year":"2022","journal-title":"Nat Struct Mol Biol"},{"key":"2024012315095442700_ref143","article-title":"Rapid protein stability prediction using deep learning representations","volume":"12","author":"Blaabjerg","journal-title":"Elife"},{"key":"2024012315095442700_ref144","doi-asserted-by":"crossref","first-page":"W132","DOI":"10.1093\/nar\/gkaa361","article-title":"MISCAST: MIssense variant to protein structure analysis web suite","volume":"48","author":"Iqbal","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref145","doi-asserted-by":"crossref","DOI":"10.3389\/fmolb.2016.00078","article-title":"The mutational landscape of the oncogenic MZF1 SCAN domain in cancer","volume":"3","author":"Nygaard","year":"2016","journal-title":"Front Mol Biosci"},{"key":"2024012315095442700_ref146","doi-asserted-by":"crossref","first-page":"14874","DOI":"10.1038\/s41598-020-71527-4","article-title":"A pan-cancer assessment of alterations of the kinase domain of ULK1, an upstream regulator of autophagy","volume":"10","author":"Kumar","year":"2020","journal-title":"Sci Rep"},{"key":"2024012315095442700_ref147","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007485","article-title":"Alterations of the interactome of Bcl-2 proteins in breast cancer at the transcriptional, mutational and structural level","volume":"15","author":"K\u00f8nig","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2024012315095442700_ref148","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmb.2022.167663","article-title":"Cancer-related mutations with local or long-range effects on an allosteric loop of p53","volume":"434","author":"Degn","year":"2022","journal-title":"J Mol Biol"},{"key":"2024012315095442700_ref149","first-page":"1","article-title":"MAVISp: Multi-layered Assessment of VarIants by Structure for proteins","author":"Arnaudi","year":"2022"},{"key":"2024012315095442700_ref150","doi-asserted-by":"crossref","first-page":"D941","DOI":"10.1093\/nar\/gky1015","article-title":"COSMIC: the catalogue of somatic mutations in cancer","volume":"47","author":"Tate","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024012315095442700_ref151","doi-asserted-by":"crossref","first-page":"pl1","DOI":"10.1126\/scisignal.2004088","article-title":"Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal","volume":"6","author":"Gao","year":"2013","journal-title":"Sci Signal"},{"key":"2024012315095442700_ref152","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1038\/s41419-022-05318-2","article-title":"The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma","volume":"13","author":"Tiberti","year":"2022","journal-title":"Cell Death Dis"},{"key":"2024012315095442700_ref153","doi-asserted-by":"crossref","DOI":"10.1016\/j.celrep.2021.110207","article-title":"Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation","volume":"38","author":"H\u00f8ie","year":"2022","journal-title":"Cell Rep"},{"key":"2024012315095442700_ref154","doi-asserted-by":"crossref","first-page":"3235","DOI":"10.1093\/molbev\/msab095","article-title":"Understanding the origins of loss of protein function by analyzing the effects of thousands of variants on activity and abundance","volume":"38","author":"Cagiada","year":"2021","journal-title":"Mol Biol Evol"},{"key":"2024012315095442700_ref155","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1186\/s12916-022-02287-3","article-title":"Targeted therapies for cancer","volume":"20","author":"Zhou","year":"2022","journal-title":"BMC Med"},{"key":"2024012315095442700_ref156","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1186\/s13045-018-0624-2","article-title":"Past, present, and future of Bcr-Abl inhibitors: from chemical development to clinical efficacy","volume":"11","author":"Rossari","year":"2018","journal-title":"J Hematol Oncol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/2\/bbad519\/56326037\/bbad519.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/2\/bbad519\/56326037\/bbad519.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T15:12:11Z","timestamp":1706022731000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad519\/7584784"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,22]]},"references-count":156,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,1,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad519","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,3,1]]},"published":{"date-parts":[[2024,1,22]]},"article-number":"bbad519"}}