115 citations · 369 across the 83 of their papers we have counts for
9 papers · 1 filter
CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMs
Yiqing Zhang, Xiaozhong Liu, Fabricio Murai
Many existing models for clinical trial outcome prediction are optimized using task-specific loss functions on trial phase-specific data. While this scheme may boost prediction for…
MEXA-CTP: Mode Experts Cross-Attention for Clinical Trial Outcome Prediction
Yiqing Zhang, Xiaozhong Liu, Fabricio Murai
Clinical trials are the gold standard for assessing the effectiveness and safety of drugs for treating diseases. Given the vast design space of drug molecules, elevated financial c…
ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning
Yang Wu, Huayi Zhang, Yizheng Jiao +6
Instruction tuning has underscored the significant potential of large language models (LLMs) in producing more human controllable and effective outputs in various domains. In this…
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model
Tianqianjin Lin, Pengwei Yan, Kaisong Song +7
Graph foundation models (GFMs) have recently gained significant attention. However, the unique data processing and evaluation setups employed by different studies hinder a deeper u…
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery
Pengwei Yan, Kaisong Song, Zhuoren Jiang +4
While self-supervised graph pretraining techniques have shown promising results in various domains, their application still experiences challenges of limited topology learning, hum…
Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph
Tianqianjin Lin, Kaisong Song, Zhuoren Jiang +6
Heterogeneous graph neural networks have become popular in various domains. However, their generalizability and interpretability are limited due to the discrepancy between their in…
Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction
Yang Wu, Xurui Li, Xuhong Zhang +3
Positive-Unlabeled (PU) Learning is a challenge presented by binary classification problems where there is an abundance of unlabeled data along with a small number of positive data…
Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning
Jiannan Guo, Yangyang Kang, Yu Duan +6
While annotating decent amounts of data to satisfy sophisticated learning models can be cost-prohibitive for many real-world applications. Active learning (AL) and semi-supervised…
AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data
Zheng Gao, Lin Guo, Chi Ma +6
Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising. The recent trend in these…