18 citations · 33 across the 17 of their papers we have counts for
20 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…
Scientific Data Skills: Enabling Agent-Ready Scientific Data Services at Scale
Xiaohan Huang, Qingqing Long, Xiaolei Du +8
Scientific data are increasingly used by AI agents, yet existing dataset representations provide limited support for reliable dataset discovery and interpretation, constraining the…
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…
ScienceDB AI: An LLM-Driven Agentic Recommender System for Large-Scale Scientific Data Sharing Services
Qingqing Long, Haotian Chen, Chenyang Zhao +6
The rapid growth of AI for Science (AI4S) has underscored the significance of scientific datasets, leading to the establishment of numerous national scientific data centers and sha…
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…
Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization
Xiaohan Huang, Dongjie Wang, Zhiyuan Ning +7
Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream ma…
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…
Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning
Weiliang Zhang, Xiaohan Huang, Yi Du +5
Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task. Among filter, wrappe…
FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies
Tianqi He, Xiaohan Huang, Yi Du +6
Feature Transformation is crucial for classic machine learning that aims to generate feature combinations to enhance the performance of downstream tasks from a data-centric perspec…
SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models
Chuan Qin, Xin Chen, Chengrui Wang +13
In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discov…
Comprehensive Metapath-based Heterogeneous Graph Transformer for Gene-Disease Association Prediction
Wentao Cui, Shoubo Li, Chen Fang +4
Discovering gene-disease associations is crucial for understanding disease mechanisms, yet identifying these associations remains challenging due to the time and cost of biological…
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…
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…
Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy
Xiaohan Huang, Dongjie Wang, Zhiyuan Ning +6
Tabular data optimization methods aim to automatically find an optimal feature transformation process that generates high-value features and improves the performance of downstream…
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations
Qingqing Long, Zheng Fang, Chen Fang +3
Traffic flow forecasting is a fundamental research issue for transportation planning and management, which serves as a canonical and typical example of spatial-temporal predictions…
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective
Yuchen Yan, Peiyan Zhang, Zheng Fang +1
The "Graph pre-training and fine-tuning" paradigm has significantly improved Graph Neural Networks(GNNs) by capturing general knowledge without manual annotations for downstream ta…
COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing
Yuqi Li, Qingqing Long, Yihang Zhou +6
Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios. While considerable success has been achieved, the…