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

Xin Cao; Yigang Chen; Jiatong Xu; Ziyue Zhang; Xiang Cheng; Shenyu Wang; Yangyi Zhang; Xiaoxuan Cai

cs.LGSep 18, 2026

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

Authors: Xin Cao, Yigang Chen, Jiatong Xu, Ziyue Zhang, Xiang Cheng, Shenyu Wang, Yangyi Zhang, Xiaoxuan Cai, +6 more

Organizations: School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong 518172, China · School of Data Science, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong 518172, China · Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong 518172, China · Guangdong Provincial Key Laboratory of Digital Biology and Drug Development, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong 518172, China · Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, P.R. China

Abstract

Drug mechanism-of-action (MoA) modeling commonly relies on perturbational transcriptomes, but matched microRNA (miRNA) measurements are often unavailable. Inferred regulatory features offer a scalable way to reuse these data. Here, we present MIRCID, a framework comparing gene expression with inferred transcription factor (TF) activity and miRNA expression across pathway classification and similarity-based MoA retrieval. HubmiRNet infers 414 pan-cancer hub miRNAs (HubmiRs) from 977 L1000 landmark genes, achieving a Pearson correlation coefficient of 87.72%; its 1,298-output variant also outperformed SiCmiR on the full-miRNA task (71.21% versus 67.30%). In the evaluated comparisons, miRNA augmentation provided more consistent gains than TF activity. Generic embedding controls showed model-dependent utility, while complementarity analyses identified a distinct, partially linearly recoverable representation that retained gene-derived structure. Illustrative rescue cases linked improved classification to biologically plausible miRNA patterns in samples with weak transcriptional signatures. These findings support inferred HubmiRs as a biologically informed recoding of transcriptomic data for perturbational drug modeling, while leaving recovery of measured perturbational miRNA responses to further validation.

Figures & tables

Figure 1: Workflow of the proposed framework for integrating regulatory layers into drug mechanistic modeling. The workflow comprises four stages: data integration; miRNA/TF regulatory feature inference (including HubmiRNet); systematic comparison of regulatory feature layers across downstream perturbational drug modeling tasks; and model training with performance and feature-contribution evaluation.

Figure 2: HubmiRNet enables accurate inference of miRNA expression from mRNA profiles. a, mRNA–miRNA co-expression basis and model development. Building on the 414 hub miRNAs identified in SiCmiR 5 , we benchmarked multiple architectures for inferring miRNA expression from L1000 landmark-gene inputs. b, Pearson correlation coefficient (PCC) and root mean square error (RMSE) for the 414-HubmiR architecture benchmark and for a matched comparison between SiCmiR and a full-output HubmiRNet variant on the original 1,298-miRNA SiCmiR task. c, Principal component analysis of predicted and observed 414-HubmiR profiles, showing overlap in their global variance structure.

Figure 3: Systematic benchmarking identifies a robust TF activity inference configuration. a, Benchmarking workflow for TF activity (TFA) inference: gene expression profiles are combined with alternative prior knowledge networks (PKNs) to evaluate three representative TFA algorithms under a unified pipeline. b, Summary of benchmarking results across three perturbation datasets, showing that the DoRothEA+Priori configuration achieves the best or second-best performance for most evaluation metrics.

Figure 4: miRNA features consistently improve drug pathway inference performance across models. a, Pathway-classification workflow using Gene alone or Gene combined with inferred TF activity (TFA), inferred HubmiR expression, or both. b,c, Held-out accuracy ( b ) and macro-F1 ( c ) for random forest (RF), multilayer perceptron (MLP), Kolmogorov–Arnold network (KAN), and residual network (ResNet) classifiers. Points denote individual held-out runs; boxes show the median and interquartile range, whiskers the minimum and maximum, and diamonds with bars the mean $\pm$ s.d. Dashed lines show the mean gene-only PROGENy linear-model performance across 7 splits.

Figure 5: Generic embedding controls and complementary geometry of the inferred HubmiR representation. a, Held-out macro-F1 for RF and ResNet using Gene, Gene plus 414-dimensional principal-component analysis (PCA), autoencoder (AE), or random projection (RP), HubmiR alone, or Gene+HubmiR. Controls used the same 977 landmark-gene inputs; RP is averaged over five projections. b, Regularized canonical correlation analysis (CCA) between Gene and each representation across the first ten held-out modes (mean and bootstrap 95% CI; 20 splits). c, Debiased linear and radial-basis-function centred-kernel alignment (CKA) with Gene (seven evenly spaced display splits from 20). d, Held-out variance-weighted

R^{2}

from cross-validated multi-output ridge regression predicting HubmiR from full Gene or the 977-gene input for the same seven splits. e, Paired change in held-out macro-F1 after adding HubmiR to Gene or Gene to HubmiR across RF, MLP, KAN, and ResNet. In a , boxes show the median and interquartile range and whiskers the observed range; in e , diamonds denote means and lines the observed range.

Figure 6: Inferred HubmiR features rescue pathway classification across EGFR, MAPK, and hypoxia perturbations. The schematic summarizes the represented signaling contexts. Heatmaps show nine strict-rescue cases (three per pathway) in which Gene-only ResNet was incorrect and Gene+HubmiR was correct on the same held-out split. Each case displays eight pathway-associated PROGENy genes and three pathway-related inferred HubmiRs as training-fold empirical-percentile deviations ( $-1$ to $+1$ ; 0, training-fold median). Arrows denote activating or inhibiting perturbations; red and green frames identify the displayed gene and HubmiR features, respectively. When pathway-related gene signals were weak, the corresponding pathway-related HubmiRs showed larger deviations, supporting rescue by complementary miRNA features.

Algorithm	Feature Space	A375	A549	HA1E	HCC515	MCF7	VCAP
weightedKS	LM+TF	0.0459	0.0182	0.0514	0.0090	0.0546	0.0117
	LM only	0.0668	0.0272	0.0636	0.0343	0.0990	0.0188
	LM+Mi	0.0803	0.0377	0.0622	0.0465	0.1037	0.0264
	LM+Mi+TF	0.0523	0.0169	0.0509	0.0094	0.0586	0.0226
KS	LM+TF	0.0394	0.0160	0.0275	0.0029	0.0533	0.0165
	LM only	0.0662	0.0201	0.0643	0.0197	0.0829	0.0203

Table 1: Drug MoA similarity prediction performance (

\mathrm{AUC}_{0.01}

) across four algorithms, four feature-selection strategies, and six cell lines. Bold and underlined values indicate the best and second-best results, respectively, for each algorithm. LM: landmark gene signature; Mi: inferred miRNA signature; TF: inferred transcription factor signature.

Figure S1: Embedding-control performance across learners and linear recoverability of inferred HubmiR features. a, Held-out pathway-classification accuracy for RF, MLP, KAN, and ResNet. b, Held-out macro-F1 for KAN and MLP. Six configurations are compared: Gene, Gene augmented with PCA, AE, or RP, HubmiR alone, and Gene+HubmiR ( $n=7$ runs per configuration). Gene and Gene+HubmiR reuse Figure 4 results. RP denotes the mean across five projection seeds within each run. Boxes show medians and interquartile ranges, whiskers the observed range, circles individual runs, and diamonds the mean $\pm$ s.d. c, AE training and validation reconstruction MSE, with the minimum validation error marked. d, Median held-out $R^{2}$ across 20 splits for 414 inferred miRNAs predicted from full Gene and the 977-gene input; the dashed line denotes equal recovery. e, Aggregate held-out $R^{2}$ across 20 frozen splits for cross-validated multi-output ridge models; bars show the mean $\pm$ s.d.

Figure S2: Pathway-wide rescue outcomes and selected strict-rescue catalogue across seven ResNet runs. a, Joint correctness of the paired Gene-only and Gene+HubmiR predictions across 595 held-out sample–run occurrences. Rescued denotes Gene-only incorrect and Gene+HubmiR correct; harmed denotes Gene-only correct and Gene+HubmiR incorrect. Counts and percentages use all 595 paired occurrences as the denominator. b, Rescued and harmed occurrence counts stratified by the true pathway label. c, Catalogue of the 27 selected strict-rescue profiles displayed in Supplementary Figure S3, annotated by pathway, dataset/profile identifier, perturbation, cellular context, and the incorrect Gene-only prediction. At most three profiles were selected per pathway; pathways with fewer available strict rescues were not padded. The nine profiles examined in Figure 6 were retained in the catalogue.

Figure S3: Feature profiles of selected strict-rescue cases across 11 pathways. Fold-relative empirical-percentile deviations for eight selected genes and three inferred HubmiRs across the same 27 strict-rescue profiles catalogued in Supplementary Figure S2c. Red dashed frames denote the Gene-only features, whereas green dashed frames encompass the genes together with the inferred HubmiRs available to the combined classifier. Values range from $-1$ to $+1$ , with zero denoting the corresponding outer-training-fold median; values for profiles rescued in multiple runs are averaged across their strict-rescue occurrences.

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