Author

Yelu Jiang

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Open access Sep 2026

Integrating multi-modal biological knowledge via contrastive dual-view graph learning for phosphorylation site-disease association prediction

CDVGL-PDA’s strong predictive performance across balanced, imbalanced, and low-similarity datasets is demonstrated, while case studies highlight its ability to uncover potential PDAs, illustrating its promise for advancing disease mechanism research and therapeutic target discovery.

Xiang Chen, Li-Jun Quan, Ye-Xuan Mao et al. · Bioinformatics · 0 citations

Open access Aug 2026

iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data

iDCF (Interpretable Deconvolution of Cell Fractions) is a novel framework that enforces biological topology onto deep neural networks, bridging the gap between computational inference and biological intuition.

Hongming Guo, Ting-Fang Wu, Wen-Zheng Wang et al. · PLoS Computational Biology · 0 citations

Jul 2026

A Structure-Aware Multimodal Framework for Drug–Target Interaction Prediction via Heterogeneous Graph Learning

Predicting drug–target interactions is critical for drug discovery, yet many deep learning methods overlook atom–residue–level relationships, so PHGDTI is proposed, a multimodal framework that integrates sequence and structural cues for binding prediction.

Hua Qian, Deng Pan, Liangpeng Nie et al. · Journal of Computational Bio... · 0 citations