{"doi":"10.1145/3765612.3767217","title":"Identification of Condition-Specific Exosomal miRNA Modules Using a Bayesian Framework","abstract":"Exosomal miRNAs play a crucial role in intercellular communication and have been implicated in aging-related retinal pathology. However, most existing methods for identifying miRNA modules focus on co-expression within a single condition, potentially missing key regulatory interactions that arise from condition-specific differences. In this study, we introduce a novel approach for miRNA module identification by redefining node connectivity to integrate differential miRNA co-expression across multiple conditions. We employ a Bayesian inference framework with a Markov Chain Monte Carlo (MCMC) sampling strategy to simultaneously estimate the number of modules and miRNA-module membership. Through simulation studies and application to two publicly available miRNA expression datasets, we have demonstrated that our method is more effective in recovering the underlying miRNA modules, compared to other existing methods. Furthermore, when applied to RNA-Seq data from retinal pigment epithelium (RPE)-derived exosomal miRNAs, our approach identified distinct miRNA modules associated with aging. Notably, a top-scoring module composed of miR-21, miR-142, miR-183, and miR-17 exhibited condition-dependent connectivity patterns. Functional validation in primary mouse microglia revealed that while individual miRNAs did not significantly alter TNFα expression, their combined transfection induced a significant upregulation, highlighting cooperative regulatory mechanisms. Our study provides new insights into exosomal miRNA-mediated gene regulation and underscores the importance of considering condition-dependent miRNA co-expression patterns. The proposed framework offers a powerful tool for identifying biologically relevant miRNA modules, with potential applications in disease biomarker discovery and therapeutic intervention strategies.","journal":null,"year":2025,"id":586271,"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":0.9587,"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":1500289,"name":"Daniel J. Wu","orcid":"0009-0008-8447-0013","position":1,"is_corresponding":false},{"id":356579,"name":"Sarah E. Bounds","orcid":"0009-0001-2419-1062","position":2,"is_corresponding":false},{"id":328833,"name":"Jiyang Cai","orcid":"0000-0003-4755-5452","position":3,"is_corresponding":false},{"id":356583,"name":"Yin Liu","orcid":"0000-0002-0349-7636","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T02:59:28.666390Z","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":[]}