{"doi":"10.1093/bioinformatics/btab285","title":"PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma","abstract":"MOTIVATION: Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, particularly in predictive modeling of disease outcomes. Moreover, because biological array data are generally represented in a non-grid structured format, CNNs cannot be applied directly. RESULTS: To address these issues, we propose a novel method, called PathCNN, that constructs an interpretable CNN model on integrated multi-omics data using a newly defined pathway image. PathCNN showed promising predictive performance in differentiating between long-term survival (LTS) and non-LTS when applied to glioblastoma multiforme (GBM). The adoption of a visualization tool coupled with statistical analysis enabled the identification of plausible pathways associated with survival in GBM. In summary, PathCNN demonstrates that CNNs can be effectively applied to multi-omics data in an interpretable manner, resulting in promising predictive power while identifying key biological correlates of disease. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at: https://github.com/mskspi/PathCNN.","journal":"Bioinformatics","year":2021,"id":153911,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":66,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9473,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":379110,"name":"Wookjin Choi","orcid":"0000-0001-8038-5876","position":1,"is_corresponding":false},{"id":652897,"name":"Euiseong Ko","orcid":"0000-0002-8522-5334","position":2,"is_corresponding":false},{"id":652898,"name":"Mingon Kang","orcid":"0000-0002-9565-9523","position":3,"is_corresponding":false},{"id":268388,"name":"Allen Tannenbaum","orcid":"0000-0002-0567-5256","position":4,"is_corresponding":false},{"id":87007,"name":"Joseph O. Deasy","orcid":"0000-0002-9437-266X","position":5,"is_corresponding":false},{"id":307645,"name":"Jung Hun Oh","orcid":"0000-0001-8791-2755","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":null,"created_at":"2026-07-18T23:43:49.684414Z","pmid":"34252964","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":[]}