678 citations · 2.5k across the 19 of their papers we have counts for
19 papers
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…
Enabling tabular deep learning when with an auxiliary knowledge graph
Camilo Ruiz, Hongyu Ren, Kexin Huang +1
Machine learning models exhibit strong performance on datasets with abundant labeled samples. However, for tabular datasets with extremely high -dimensional features but limited…
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
Kexin Huang, Ying Jin, Emmanuel Candès +1
Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their reliable de…
TuneUp: A Simple Improved Training Strategy for Graph Neural Networks
Weihua Hu, Kaidi Cao, Kexin Huang +4
Despite recent advances in Graph Neural Networks (GNNs), their training strategies remain largely under-explored. The conventional training strategy learns over all nodes in the or…
Graph Representation Learning in Biomedicine
Michelle M. Li, Kexin Huang, Marinka Zitnik
Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scient…
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Kexin Huang, Tianfan Fu, Wenhao Gao +7
Therapeutics machine learning is an emerging field with incredible opportunities for innovatiaon and impact. However, advancement in this field requires formulation of meaningful l…
HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data
Tianfan Fu, Kexin Huang, Cao Xiao +2
Clinical trials are crucial for drug development but are time consuming, expensive, and often burdensome on patients. More importantly, clinical trials face uncertain outcomes due…
An Interpretable End-to-end Fine-tuning Approach for Long Clinical Text
Kexin Huang, Sankeerth Garapati, Alexander S. Rich
Unstructured clinical text in EHRs contains crucial information for applications including decision support, trial matching, and retrospective research. Recent work has applied BER…
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
Kexin Huang, Tianfan Fu, Dawood Khan +7
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics. Deep learning (DL) has demonstrated remarkable progress in predicting drug ef…
SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization
Yue Yu, Kexin Huang, Chao Zhang +3
Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning…
scGNN: scRNA-seq Dropout Imputation via Induced Hierarchical Cell Similarity Graph
Kexin Huang
Single-cell RNA sequencing provides tremendous insights to understand biological systems. However, the noise from dropout can corrupt the downstream biological analysis. Hence, it…
Graph Meta Learning via Local Subgraphs
Kexin Huang, Marinka Zitnik
Prevailing methods for graphs require abundant label and edge information for learning. When data for a new task are scarce, meta-learning can learn from prior experiences and form…
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction
Kexin Huang, Cao Xiao, Lucas Glass +1
Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery, which is costly and time-consuming due to the need of experimental search over large d…
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks
Kexin Huang, Cao Xiao, Lucas Glass +2
Molecular interaction networks are powerful resources for the discovery. They are increasingly used with machine learning methods to predict biologically meaningful interactions. W…
DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
Kexin Huang, Tianfan Fu, Lucas Glass +3
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However,…
Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation
Kexin Huang, Abhishek Singh, Sitong Chen +4
Clinical notes contain rich data, which is unexploited in predictive modeling compared to structured data. In this work, we developed a new text representation Clinical XLNet for c…
CASTER: Predicting Drug Interactions with Chemical Substructure Representation
Kexin Huang, Cao Xiao, Trong Nghia Hoang +2
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality. Identifying potential DDIs during the drug design process is critical for patients and soci…
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Kexin Huang, Jaan Altosaar, Rajesh Ranganath
Clinical notes contain information about patients that goes beyond structured data like lab values and medications. However, clinical notes have been underused relative to structur…
EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection
Kexin Huang, Rodrigo Nogueira
Epistasis (gene-gene interaction) is crucial to predicting genetic disease. Our work tackles the computational challenges faced by previous works in epistasis detection by modeling…