5 citations · 15 across the 7 of their papers we have counts for
7 papers
Validation of an LLM-based Multi-Agent Framework for Protein Engineering in Dry Lab and Wet Lab
Zan Chen, Yungeng Liu, Yu Guang Wang +1
Recent advancements in Large Language Models (LLMs) have enhanced efficiency across various domains, including protein engineering, where they offer promising opportunities for dry…
TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering
Yungeng Liu, Zan Chen, Yu Guang Wang +1
The exponential growth in protein-related databases and scientific literature, combined with increasing demands for efficient biological information retrieval, has created an urgen…
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering
Yungeng Liu, Zan Chen, Yu Guang Wang +1
Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive. While deep learning (DL) models have shown p…
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models
Jiayi Guo, Zan Chen, Yingrui Ji +4
Automated Machine Learning (AutoML) has simplified complex ML processes such as data pre-processing, model selection, and hyper-parameter searching. However, traditional AutoML fra…
A Survey for Large Language Models in Biomedicine
Chong Wang, Mengyao Li, Junjun He +14
Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicin…
TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering
Yiqing Shen, Zan Chen, Michail Mamalakis +6
The structural similarities between protein sequences and natural languages have led to parallel advancements in deep learning across both domains. While large language models (LLM…
A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding
Yiqing Shen, Zan Chen, Michail Mamalakis +6
The parallels between protein sequences and natural language in their sequential structures have inspired the application of large language models (LLMs) to protein understanding.…