12 citations · 15 across the 7 of their papers we have counts for
8 papers
LabAgent: Customize Any Research Hubs for Scientific Discoveries Using AI Agents
Lei Liu, Yikun Zhang, Jialin Chen +7
Scientific research is a continuous process that emphasizes inheritance. Methods developed by predecessors are often expanded upon by new researchers to explore more novel and in-d…
Benchmarking AI Agents for Addressing Scientific Challenges Across Scales
Tianyu Liu, Allen Xin Wang, Antonia Panescu +30
AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchma…
A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization
Tianyu Liu, Wangjie Zheng, Rui Yang +12
Accurate and timely diagnosis is essential for effective treatment, particularly in the context of rare diseases. However, current diagnostic workflows often lead to prolonged asse…
DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery
Tianyu Liu, Sihan Jiang, Fan Zhang +3
Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generati…
TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots
Tianyu Liu, Weihao Xuan, Hao Wu +15
Advances in AI have introduced several strong models in computational pathology to usher it into the era of multi-modal diagnosis, analysis, and interpretation. However, the curren…
Advancing AI Research Assistants with Expert-Involved Learning
Tianyu Liu, Simeng Han, Hanchen Wang +27
Large language models (LLMs) and large multimodal models (LMMs) promise to accelerate biomedical discovery, yet their reliability remains unclear. We introduce ARIEL (AI Research A…
Geneverse: A collection of Open-source Multimodal Large Language Models for Genomic and Proteomic Research
Tianyu Liu, Yijia Xiao, Xiao Luo +3
The applications of large language models (LLMs) are promising for biomedical and healthcare research. Despite the availability of open-source LLMs trained using a wide range of bi…
MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
Tianyu Liu, Yuge Wang, Rex Ying +1
Discovering genes with similar functions across diverse biomedical contexts poses a significant challenge in gene representation learning due to data heterogeneity. In this study,…