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Author

Hyunju Lee

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Open access Sep 2026

ST-ConMa: a multimodal foundation framework for spatial transcriptomics via image–gene contrastive and matching learning

Abstract Spatial transcriptomics (ST) enables the spatial mapping of gene expression by combining tissue images with spot-level gene expression profiles, allowing for the analysis of gene distribution and interactions between adjacent regions. Although numerous deep learning models have been developed for ST, most exis...

Taelim Yeom, Dongha Choi, Hyunju Lee · 0 citations
Open access Sep 2026

InFo-DRP: Integrating Invariant Learning and Foundation Representations for Unseen Drug Response Prediction.

Accurate prediction of cancer cell responses to chemical compounds remains a fundamental challenge in precision oncology and drug discovery. Although several drug response prediction (DRP) models have been proposed, most were evaluated using random splits of drug-cell line pairs, allowing the same drugs to appear in bo...

Ji-Sang Lee, Taelim Yeom, June-Woo Kim et al. · 0 citations
Open access Jul 2026

AlphaDTA: integrating AlphaFold3 embeddings and 3D complex structures for drug-target binding affinity prediction.

By integrating AlphaFold3-derived single, pair, and structure-based embeddings through adaptive fusion, AlphaDTA improves generalization on structurally nonredundant benchmarks and demonstrates utility for target-specific drug repurposing.

Minjae Chung, Sejin Park, Hyunju Lee · 0 citations

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