12 citations · 12 across the 11 of their papers we have counts for
13 papers
From Scientific Observations to Mechanisms: Benchmarking Hypothesis Generation by AI Scientists
Xiaxun Xie, Qingqing Long, Meng Xiao +4
Data-driven mechanistic hypotheses are essential to scientific discovery because they explain how underlying processes produce observed phenomena. AI agents and AI scientists incre…
BioHarness: Substrate-Aware Evidence Assembly for Biomedical Question Answering across Literature, Knowledge Bases, and Biological Atlases
Meng Xiao, Chuan Qin, Jinmiao Chen +3
Motivation: Biomedical question answering often requires evidence beyond topically retrieved literature, including gene alias resolution, database identifier normalization, and atl…
From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching
Siyu Pu, Qingqing Long, Xiaohan Huang +7
Single-cell RNA sequencing (scRNA-seq) provides high-dimensional profiles of cellular states, enabling data-driven modeling of cellular dynamics over time. In practice, time-resolv…
DeepEra: A Deep Evidence Reranking Agent for Scientific Retrieval-Augmented Generated Question Answering
Haotian Chen, Qingqing Long, Siyu Pu +6
With the rapid growth of scientific literature, scientific question answering (SciQA) has become increasingly critical for exploring and utilizing scientific knowledge. Retrieval-A…
SciHorizon-GENE: Benchmarking LLM for Life Sciences Inference from Gene Knowledge to Functional Understanding
Xiaohan Huang, Meng Xiao, Chuan Qin +4
Large language models (LLMs) have shown growing promise in biomedical research, particularly for knowledge-driven interpretation tasks. However, their ability to reliably reason fr…
SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generation with LLMs
Haotian Chen, Qingqing Long, Meng Xiao +6
Scientific literature question answering is a pivotal step towards new scientific discoveries. Recently, \textit{two-stage} retrieval-augmented generated large language models (RAG…
Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training
Meng Xiao, Xunxin Cai, Qingqing Long +3
Corpus distillation for biomedical large language models (LLMs) seeks to address the pressing challenge of insufficient quantity and quality in open-source annotated scientific cor…
Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training
Xunxin Cai, Chengrui Wang, Qingqing Long +2
The rapid advancement of large language models (LLMs) in biological-medical applications has highlighted a gap between their potential and the limited scale and often low quality o…
scReader: Prompting Large Language Models to Interpret scRNA-seq Data
Cong Li, Qingqing Long, Yuanchun Zhou +1
Large language models (LLMs) have demonstrated remarkable advancements, primarily due to their capabilities in modeling the hidden relationships within text sequences. This innovat…
GeneSUM: Large Language Model-based Gene Summary Extraction
Zhijian Chen, Chuan Hu, Min Wu +4
Emerging topics in biomedical research are continuously expanding, providing a wealth of information about genes and their function. This rapid proliferation of knowledge presents…
Towards Graph Prompt Learning: A Survey and Beyond
Qingqing Long, Yuchen Yan, Peiyan Zhang +12
Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, imag…
BioRAG: A RAG-LLM Framework for Biological Question Reasoning
Chengrui Wang, Qingqing Long, Meng Xiao +5
The question-answering system for Life science research, which is characterized by the rapid pace of discovery, evolving insights, and complex interactions among knowledge entities…
GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation
Guojiao Lin, Zhen Meng, Dongjie Wang +3
Multimodal recommendation systems (MMRS) have received considerable attention from the research community due to their ability to jointly utilize information from user behavior and…