{"doi":"10.1101/2025.07.10.664265","title":"Auto-MedCalc: Automated Biomarkers Discovery and Risk Score Generation with AI Agents","abstract":"Identifying biomarkers and generating risk scores are usually essential tasks in many biomedicine and clinical scenarios. However, this is a highly hypothesis-driven and experience-dependent process requiring extensive experiments as well. How to discover biomarkers from multi-modality, multi-source data and generate risk scores with higher precision motivates us to design the framework Auto-MedCalc, a data-driven pipeline to automatically identify biomarkers and generate risk scores for diagnosis. Auto-MedCalc is a multi-agent AI system with large language models and computational tools. Three representative studies of transplantation from the public ImmPort data portal are used for the experiment. Auto-MedCalc achieved 0.93, 0.88, and 0.88 ROC-AUC on the rejection prediction after kidney, liver, and heart transplants, surpassing the best expert-designed medical calculators by 50%, 4.7%, and 39%, respectively. Auto-MedCalc also validated the previously found human biomarkers and discovered new biomarkers, β-Glucuronidase and Alpha-1-microglobulin, in heart transplants, which hadn’t attracted much attention before. The superior performance from experiments shows that Auto-MedCalc can be used for both clinical applications and scientific discovery.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":4393,"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":0.0437,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-07-16","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":3336,"name":"Sanchita Bhattacharya","orcid":"0000-0002-3056-0733","position":1,"is_corresponding":false},{"id":51,"name":"Atul Janardhan Butte","orcid":"0000-0002-7433-2740","position":2,"is_corresponding":false},{"id":45207,"name":"Sirui Ding","orcid":null,"position":0,"is_corresponding":true}],"reference_count":62,"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":[]}