Tao Qin

Carbon-A: Unified Genome Annotation across Eukaryotic Diversity

Carbon-A annotates protein-coding genes across eukaryotic genomes using a large encoder model, achieving high accuracy and enabling the creation of a comprehensive annotation database.

Reconstructing sequence-grammar trajectories enables interpretable and tunable cis-regulatory element design

This paper introduces GO-CRE, a method for designing cell-type-specific cis-regulatory elements by reconstructing sequence-grammar trajectories and using reinforcement learning with predictor guidance.

HoloCell: A Generative Foundation Model for Holistic Cellular Modeling

HoloCell is a generative foundation model that integrates epigenomics, transcriptomics, and proteomics data across single-cell multi-omics modalities, demonstrating superior performance in representation learning and cross-modal generation.

GENERator-v2: Reconciling Coarse Tokenization with Single-Nucleotide Resolution in Genomic Language Modeling

This paper introduces improved genomic language models that balance long-range context with single-nucleotide resolution, achieving better performance and efficiency than prior methods.

Ophiuchus-Ab: A Versatile Generative Foundation Model for Advanced Antibody-Based Immunotherapy

Ophiuchus-Ab is a generative foundation model for antibody design that captures inter-chain dependencies and predicts antibody properties like developability and binding affinity.

Phenotype-Guided In Silico Molecular Generation Using Large Language Models

GEMGen generates small molecules from transcriptomic data to phenocopy gene knockdown effects and identify novel inhibitors.

FlexRibbon: Joint Sequence and Structure Pretraining for Protein Modeling

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