arXiv Machine Learning September 21, 2026 By Xin Cao, Yigang Chen, Jiatong Xu, Ziyue Zhang, Xiang Cheng, Shenyu Wang, Yangyi Zhang, Xiaoxuan Cai, Shidong Cui, Zihao Zhu, Xiang Ji, Hsi-Yuan Huang, Yang-Chi-Dung Lin, Hsien-Da Huang

MIRCID: Inferred Hub-miRNAs Drive Cross-Task Improvements in Drug Mechanistic Modeling

Read the original on arXiv Machine Learning →

MIRCID is a framework that enhances drug mechanism-of-action modeling by integrating inferred microRNA (miRNA) activity with gene expression data. It identifies 414 pan‑cancer hub miRNAs (HubmiRs) from L1000 landmark genes, achieving high correlation with actual miRNA measurements and outperforming existing methods in full‑miRNA prediction tasks. The approach consistently improves pathway classification and MoA retrieval, with miRNA augmentation yielding more reliable gains than transcription factor activity alone.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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