ArticleInternational journal of molecular sciences2026
DeepExoMir: A Reproducible RNA Language Model Framework for CLIP-Seq-Supported MicroRNA Target-Site Prioritization.
Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
MicroRNAs regulate gene expression post-transcriptionally, yet target prediction faces a credibility gap: published methods drop sharply against CLIP-seq-validated negatives. We present DeepExoMir, a deep learning framework integrating frozen RiNALMo RNA language model embeddings with biologically informed features. Under a dual-probe ablation protocol on three miRBench test sets, DeepExoMir reaches mean AU-PRC 0.855, surpassing eight retrained baselines (paired bootstrap p<0.001). Evolutionary conservation and duplex structure prove largely redundant with language-model priors, motivating a structure-free Lite variant (0.863). On nine exosomal miRNAs from a companion melanogenesis study, DeepExoMir recovers literature-validated targets and ranks canonical pigmentation regulators (KITLG, MITF, TYRP1) in the top 5%.
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