{"doi":"10.1093/bioinformatics/btae257","title":"Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks","abstract":"<jats:title>Abstract</jats:title>\n               <jats:sec>\n                  <jats:title>Motivation</jats:title>\n                  <jats:p>Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited.</jats:p>\n               </jats:sec>\n               <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes.</jats:p>\n               </jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation</jats:title>\n                  <jats:p>DISHyper is freely available for download at https://github.com/genemine/DISHyper.</jats:p>\n               </jats:sec>","journal":"Bioinformatics","year":2024,"id":598676,"datarank":0.38474240361923057,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"self_citation_contribution":0.38474240361923057,"citation_network_contribution":0.0,"self_endowment_contribution":0.38474240361923057,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":790527,"name":"Hong-Dong Li","orcid":"0000-0003-3438-739X","position":1,"is_corresponding":false},{"id":1534302,"name":"Li-Shen Zhang","orcid":null,"position":2,"is_corresponding":false},{"id":1229235,"name":"Yiwei Liu","orcid":"0000-0003-4136-8146","position":3,"is_corresponding":false},{"id":118865,"name":"Yaohang Li","orcid":null,"position":4,"is_corresponding":false},{"id":308453,"name":"Jianxin Wang","orcid":"0000-0003-1516-0480","position":5,"is_corresponding":false},{"id":109403,"name":"Chao Deng","orcid":"0000-0002-1994-949X","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks","abstract":"<jats:title>Abstract</jats:title>\n               <jats:sec>\n                  <jats:title>Motivation</jats:title>\n                  <jats:p>Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited.</jats:p>\n               </jats:sec>\n               <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes.</jats:p>\n               </jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation</jats:title>\n                  <jats:p>DISHyper is freely available for download at https://github.com/genemine/DISHyper.</jats:p>\n               </jats:sec>","is_dataset_classified":null,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38940121","pmcid":null,"openalex_id":"https://openalex.org/W4400140646","authors":[],"funders":[{"funder_name":"National Key Research and Development Program of China","grant_id":"2021YFF1201200","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"62350004","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"62332020","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"U22A2041","title":null},{"funder_name":"Science Foundation for Distinguished Young Scholars of Hunan Province","grant_id":"2023JJ10080","title":null},{"funder_name":"High-Performance Computing Center of Central South University","grant_id":"","title":null}],"total_grants":6,"fwci":1.9985,"citation_percentile":0.86724722,"influential_citations":0,"citation_trend":[{"year":2024,"count":3},{"year":2025,"count":7},{"year":2026,"count":2}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.1093/bioinformatics/btae257","host_type":"journal"},{"url":"https://doi.org/10.1093/bioinformatics/btae257","host_type":"publisher"},{"url":"https://academic.oup.com/bioinformatics/article-pdf/40/Supplement_1/i511/58354644/btae257.pdf","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38940121","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11211849","host_type":"repository"},{"url":"https://digitalcommons.odu.edu/computerscience_fac_pubs/334","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11211849/pdf/btae257.pdf","host_type":"repository"}],"fields_of_study":["Bioinformatics and Genomic Networks","Ferroptosis and cancer prognosis","Gene expression and cancer classification"],"mesh_terms":["Humans","Neoplasms","Neural Networks, Computer","Computational Biology","Genomics","Databases, Genetic","Genes, Neoplasm","Molecular Sequence Annotation"],"keywords":["Hypergraph","Gene","Computational biology","ENCODE","Genomics","Cancer","Computer science","Gene regulatory network","Artificial neural network","Biology","Genome","Genetics","Artificial intelligence","Mathematics","Gene expression"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-28T16:06:01.949617Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}