{"doi":"10.1371/journal.pcbi.1014015","title":"CA-CAE: A deep learning-based multi-omics model for pan-cancer subtype classification and prognosis prediction","abstract":"<jats:p>In cancer research, identifying cancer subtypes and evaluating prognosis are crucial for personalized diagnosis and treatment of cancer. With the advancement of high-throughput sequencing technologies, multi-omics data has become essential for cancer classification and prognostic analysis. By integrating deep learning techniques, it is possible to more accurately identify cancer subtypes, providing a robust basis for personalized treatment of cancer patients. In this study, we propose a convolutional autoencoder prognostic model incorporating a channel attention mechanism (CA-CAE). The model utilizes multi-omics data to predict survival-associated cancer subtypes and identify prognostic genes. We applied CA-CAE to multiple cancer types, successfully identifying subtypes in 15 distinct cancer types and revealing significant survival differences among these subtypes. Moreover, compared to traditional statistical methods and other deep learning approaches, CA-CAE demonstrated superior performance in predicting survival outcomes.</jats:p>","journal":"PLOS Computational Biology","year":2026,"id":601992,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"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":1543719,"name":"Yicheng Lu","orcid":"0009-0007-9475-2391","position":1,"is_corresponding":false},{"id":1543720,"name":"Peixian Li","orcid":null,"position":2,"is_corresponding":false},{"id":1543721,"name":"Junxuan Wu","orcid":null,"position":3,"is_corresponding":false},{"id":1043079,"name":"Guohua Wang","orcid":"0000-0002-4810-8534","position":4,"is_corresponding":false},{"id":287893,"name":"Wen Yang","orcid":"0000-0002-1817-4194","position":5,"is_corresponding":false},{"id":1543718,"name":"Shumei Zhang","orcid":"0000-0003-3946-2977","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"CA-CAE: A deep learning-based multi-omics model for pan-cancer subtype classification and prognosis prediction","abstract":"<jats:p>In cancer research, identifying cancer subtypes and evaluating prognosis are crucial for personalized diagnosis and treatment of cancer. With the advancement of high-throughput sequencing technologies, multi-omics data has become essential for cancer classification and prognostic analysis. By integrating deep learning techniques, it is possible to more accurately identify cancer subtypes, providing a robust basis for personalized treatment of cancer patients. In this study, we propose a convolutional autoencoder prognostic model incorporating a channel attention mechanism (CA-CAE). The model utilizes multi-omics data to predict survival-associated cancer subtypes and identify prognostic genes. We applied CA-CAE to multiple cancer types, successfully identifying subtypes in 15 distinct cancer types and revealing significant survival differences among these subtypes. Moreover, compared to traditional statistical methods and other deep learning approaches, CA-CAE demonstrated superior performance in predicting survival outcomes.</jats:p>","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41719374","pmcid":"PMC12948314","openalex_id":"https://openalex.org/W7130681017","authors":[],"funders":[{"funder_name":"National Natural Science Foundation of China","grant_id":"62371117","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"62302315","title":null},{"funder_name":"National Science Fund for Distinguished Young Scholars","grant_id":"62225109","title":null},{"funder_name":"Heilongjiang Provincial Postdoctoral Science Foundation","grant_id":"LBH-Z23004","title":null},{"funder_name":"Fundamental Research Funds for the Central Universities","grant_id":"2572025JT05-03","title":null}],"total_grants":5,"fwci":4.3846,"citation_percentile":0.91059861,"influential_citations":0,"citation_trend":[{"year":2026,"count":1}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.1371/journal.pcbi.1014015","host_type":"journal"},{"url":"https://doi.org/10.1371/journal.pcbi.1014015","host_type":"publisher"},{"url":"https://dx.plos.org/10.1371/journal.pcbi.1014015","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/41719374","host_type":"repository"},{"url":"https://doaj.org/article/ff3b9adcd17d4260a4a86e808c2a7d94","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12948314/","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC12948314","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC12948314?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Bioinformatics and Genomic Networks","Ferroptosis and cancer prognosis","AI in cancer detection"],"mesh_terms":["Deep Learning","Multiomics","Predictive Learning Models","Classification Algorithms","Prediction Algorithms","Autoencoder","Humans","Neoplasms","Prognosis","Computational Biology","Genomics"],"keywords":["Cancer","Deep learning","Autoencoder","Survival analysis","Personalized medicine","Mechanism (biology)","Cancer treatment","Convolutional neural network"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T17:57:33.090108Z","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":[]}