{"doi":"10.1093/bib/bbaf728","title":"BiChemoCLAM: a weakly supervised multimodal framework for chemotherapy response prediction","abstract":"Chemotherapy is an important treatment for cancer patients, but it comes with risks. Therefore, effective chemotherapy response prediction is crucial. While whole slide image provides high-resolution insights into tumour environments, existing weakly supervised learning frameworks struggle to effectively integrate molecular data, such as gene expression, limiting their predictive power in complex chemotherapy response and small-sample scenarios. We present a bimodal chemotherapy response multi-instance learning framework, BiChemoCLAM, a novel multimodal deep learning framework that combines attention-driven multiple instance learning with multimodal compact bilinear pooling for interpretable and data-efficient chemotherapy response prediction. It achieves an Area Under Curve (AUC) of 80.91%, 71.68%, and 75.80% on ovarian serous cystadenocarcinoma, colorectal adenocarcinoma, and bladder urothelial carcinoma cancer datasets, respectively. The experimental results show that BiChemoCLAM is an effective model for predicting response to chemotherapy.","journal":"Briefings in Bioinformatics","year":2026,"id":8796,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0543,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2026-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":75589,"name":"Changming Sun","orcid":null,"position":1,"is_corresponding":false},{"id":75590,"name":"Jia Zhou","orcid":null,"position":2,"is_corresponding":false},{"id":75591,"name":"Cun Xie","orcid":null,"position":3,"is_corresponding":false},{"id":75592,"name":"Leyi Wei","orcid":null,"position":4,"is_corresponding":false},{"id":75593,"name":"Jia Zhao","orcid":null,"position":5,"is_corresponding":false},{"id":75594,"name":"Xiaofeng Liu","orcid":null,"position":6,"is_corresponding":false},{"id":75595,"name":"Ran Su","orcid":null,"position":7,"is_corresponding":false},{"id":75588,"name":"Jinglong Gui","orcid":null,"position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","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":[]}