6 citations · 13 across the 8 of their papers we have counts for
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q-bio.GN2025★ 1 cited
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…
q-bio.GN2024
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…
q-bio.GN2024★ 6 cited
DiscDiff: Latent Diffusion Model for DNA Sequence Generation
Zehui Li, Yuhao Ni, William A V Beardall +4
This paper introduces a novel framework for DNA sequence generation, comprising two key components: DiscDiff, a Latent Diffusion Model (LDM) tailored for generating discrete DNA se…