4 papers
LDP: Parameter-Efficient Fine-Tuning of Multimodal LLM for Medical Report Generation
Tianyu Zhou, Junyi Tang, Zehui Li +2
Colonoscopic polyp diagnosis is pivotal for early colorectal cancer detection, yet traditional automated reporting suffers from inconsistencies and hallucinations due to the scarci…
Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and Multi-Task Learning
Zehui Li, Vallijah Subasri, Yifei Shen +4
Large Language Models (LLMs) demonstrate remarkable generalizability across diverse tasks, yet genomic foundation models (GFMs) still require separate finetuning for each downstrea…
GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning
Zehui Li, Vallijah Subasri, Guy-Bart Stan +2
Genetic variants (GVs) are defined as differences in the DNA sequences among individuals and play a crucial role in diagnosing and treating genetic diseases. The rapid decrease in…
Absorb & Escape: Overcoming Single Model Limitations in Generating Genomic Sequences
Zehui Li, Yuhao Ni, Guoxuan Xia +4
Abstract Recent advances in immunology and synthetic biology have accelerated the development of deep generative methods for DNA sequence design. Two dominant approaches in this fi…