Yaosen Min

OmniSyn generates and optimizes target-specific molecules with synthesis traces using a sequence-conditioned LLM, achieving state-of-the-art performance on drug discovery benchmarks.

ProMeta improves disease diagnosis and prediction from plasma proteomics using a meta-learning framework that overcomes data scarcity with biological priors and interpretable features.

PRSNet-2 predicts complex phenotypes from genotypic data using a hierarchical graph neural network, outperforming baselines and reducing overfitting.

FlexRibbon is a pretrained protein model that jointly learns from sequences and structures, achieving state-of-the-art performance in mutation-rich settings.

CellNavi predicts driver genes for cell state transitions using a deep learning framework that integrates gene graphs with transcriptomics data across diverse cell types and perturbations.

DeepVirus identifies and classifies viral dark matter in metagenomic data using a hierarchical transformer framework, revealing novel viral lineages and expanding known RNA virus diversity.

STARNet infers gene regulatory networks from spatial multi-omics data, revealing domain-specific regulatory interactions in tissues.

This paper introduces MolCLE, a molecular graph contrastive learning framework with parameterized explainable augmentations, which outperforms baselines on real-world datasets with fewer parameters.

The paper introduces ContactLib-DNN, a method for detecting homologous proteins using structure fingerprints, achieving higher accuracy and speed than existing methods.