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Author

George C. Tseng

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Open access Aug 2026

Multimodal contrastive learning for integrating molecular representations and cellular phenotypes in drug-target interaction prediction

A two-stage contrastive learning framework integrating drug structures, protein sequences, and Cell Painting morphological profiles into a unified embedding space, which reveals pathway-specific morphological signatures associated with drug targets, providing biologically interpretable insights into drug mechanisms.

Ying-Ju Lai, Tianyuzhou Liang, Po-Yuan Chen et al. · 0 citations
Open access Jul 2026

An embedding-based framework enables statistical testing of gene-set function hypotheses inferred by large language models

An embedding-based statistical framework is developed that transforms gene and function descriptions into vector representations, enabling statistical testing of gene-gene and gene-function relationships and quantitative prioritization of de novo functional hypotheses inferred by LLMs.

Yanhao Tan, Li-Ju Wang, Tianyuzhou Liang et al. · 0 citations

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