10 papers
SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing
Shuyu Chen, Chen Zhu, Ye Zhang +3
Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology. Unlike bulk assays, scRNA-seq captur…
GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis
Haoyang Liu, Yijiang Li, Haohan Wang
Gene expression analysis holds the key to many biomedical discoveries, yet extracting insights from raw transcriptomic data remains formidable due to the complexity of multiple lar…
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
Haoyang Liu, Yijiang Li, Jinglin Jian +7
Machine learning has emerged as a powerful tool for scientific discovery, enabling researchers to extract meaningful insights from complex datasets. For instance, it has facilitate…
Dataset Distillation via the Wasserstein Metric
Haoyang Liu, Yijiang Li, Tiancheng Xing +5
Dataset Distillation (DD) aims to generate a compact synthetic dataset that enables models to achieve performance comparable to training on the full large dataset, significantly re…
Achilles Heel of Distributed Multi-Agent Systems
Yiting Zhang, Yijiang Li, Tianwei Zhao +3
Multi-agent system (MAS) has demonstrated exceptional capabilities in addressing complex challenges, largely due to the integration of multiple large language models (LLMs). Howeve…
GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis
Haoyang Liu, Shuyu Chen, Ye Zhang +1
Recent advancements in machine learning have significantly improved the identification of disease-associated genes from gene expression datasets. However, these processes often req…