{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T19:24:08Z","timestamp":1771615448011,"version":"3.50.1"},"reference-count":61,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:00:00Z","timestamp":1771545600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education and Training of Vietnam","award":["CT2025.EA.BKA.05"],"award-info":[{"award-number":["CT2025.EA.BKA.05"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Enhancers are critical regulatory DNA elements that, when dysregulated, can contribute to disease pathogenesis by altering gene expression. Although millions of enhancers have been identified through large-scale genomic projects, their associations with human diseases remain largely uncharacterized, emphasizing the need for robust computational approaches. In our previous work, we developed RWDisEnh, a network-based method that integrates a shared gene\u2013based enhancer network with a disease similarity network within a heterogeneous framework to predict disease\u2013enhancer associations. In this study, we present RWDisEnh+\u2009, an enhanced version of RWDisEnh that incorporates a sequence-based enhancer similarity network into a multiplex-heterogeneous network to improve prediction performance. Using an extended random walk with restart (RWR) algorithm, RWDisEnh+ allows information to propagate across disease and enhancer layers, leveraging both gene-based and sequence-based similarity features to rank candidate enhancers for each disease. Comprehensive evaluation using 3-fold cross-validation demonstrated that RWDisEnh+ achieves an average AUC of 0.874, outperforming RWDisEnh\u2019s AUC of 0.819. Moreover, RWDisEnh+ identifies a larger number of evidence-supported disease\u2013enhancer associations across top-k rankings, including 10 enhancers linked to seven diseases such as asthma, rheumatoid arthritis, and type 2 diabetes. GWAS validation and pathway enrichment analyses further reveal that these predicted associations are enriched in immune, inflammatory, and metabolic pathways, highlighting their biological relevance. Overall, RWDisEnh+ provides a stable and effective framework for predicting novel disease\u2013enhancer associations, offering new insights into enhancer-mediated gene regulation and the genetic architecture of complex diseases.<\/jats:p>","DOI":"10.1371\/journal.pone.0341861","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:40:03Z","timestamp":1771612803000},"page":"e0341861","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["RWDisEnh+: Enhancing disease-enhancer association prediction through multiplex-heterogeneous 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