{"doi":"10.1002/advs.202503289","title":"CellPhenoX: An Explainable Machine Learning Method for Identifying Cell Phenotypes To Predict Clinical Outcomes from Single‐Cell Multi‐Omics","abstract":"Single-cell technologies have transformed the understanding of disease heterogeneity, but linking cell-level phenotypic alterations to clinical outcomes becomes increasingly challenging as single-cell datasets continue to expand. This is further complicated by the lack of interpretability in existing methods and the difficulty of detecting interaction effects-nonlinear dependencies between factors like sex, age, and disease. To address this, a novel explainable machine learning method, CellPhenoX, is developed to identify cell-specific phenotypes and interaction effects linked to clinical outcomes. CellPhenoX integrates classification models, explainable artificial intelligence (AI) techniques, and a statistical framework to generate interpretable, cell-specific scores to uncover condition-associated cell populations. Extensive benchmarking and applications demonstrate the efficacy of CellPhenoX across diverse single-cell study designs, including the dedicated and disease-motivated simulations, binary disease-control comparisons, and severity-stratified patient cohorts. Notably, CellPhenoX identifies an activated monocyte phenotype in COVID-19, with expansion correlated with disease severity after adjusting for covariates and interactive effects. It also uncovers a fibroblast-specific state transition gradient predicting tissue inflammation in chronic diseases, and identifies therapy-induced T cell changes and biomarkers linked to the tumor microenvironment. By integrating interpretability into clinical classification, CellPhenoX offers a powerful framework for translating single-cell findings into clinical impact.","journal":"Advanced Science","year":2025,"id":526933,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.955,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1163318,"name":"Jun Inamo","orcid":"0000-0002-9927-7936","position":1,"is_corresponding":false},{"id":1403060,"name":"Zachary Caterer","orcid":"0000-0001-9019-0730","position":2,"is_corresponding":false},{"id":1403061,"name":"Revanth Krishna","orcid":"0000-0002-4902-8368","position":3,"is_corresponding":false},{"id":226256,"name":"Fan Zhang","orcid":"0000-0002-6102-2970","position":4,"is_corresponding":false},{"id":830123,"name":"Jade Young","orcid":"0000-0003-0887-3319","position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":null,"created_at":"2026-07-19T02:50:34.851930Z","pmid":"40985689","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":[]}