678 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of Illinois Urbana-ChampaignUS10 papers
- Georgia Institute of TechnologyUS7 papers
- Harvard UniversityUS4 papers
- Temple UniversityUS3 papers
- Peking UniversityCN2 papers
- Amgen (United States)US1 paper
- Baruch CollegeUS1 paper
- Boston UniversityUS1 paper
- Chinese Academy of Science South America Center for AstronomyCL1 paper
- IBM (United States)US1 paper
- Illinois CollegeUS1 paper
- IQVIA (United Kingdom)GB1 paper
16 papers
Chemo-Dynamical Tagging in the Outskirts: The Origins of Stellar Substructures in the Magellanic Clouds
César Muñoz, Antonela Monachesi, David L. Nidever +12
We present the first detailed chemical analysis from APOGEE-2S observations of stars in six regions of recently discovered substructures in the outskirts of the Magellanic Clouds e…
PopNet: Real-Time Population-Level Disease Prediction with Data Latency
Junyi Gao, Cao Xiao, Lucas M. Glass +1
Population-level disease prediction estimates the number of potential patients of particular diseases in some location at a future time based on (frequently updated) historical dis…
Machine Learning Applications for Therapeutic Tasks with Genomics Data
Kexin Huang, Cao Xiao, Lucas M. Glass +3
Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and devel…
FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection
Zhi Qiao, Austin Bae, Lucas M. Glass +2
To test the possibility of differentiating chest x-ray images of COVID-19 against other pneumonia and healthy patients using deep neural networks. We construct the X-ray imaging da…
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data
Chacha Chen, Junjie Liang, Fenglong Ma +3
Successful health risk prediction demands accuracy and reliability of the model. Existing predictive models mainly depend on mining electronic health records (EHR) with advanced de…
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…
STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidence
Junyi Gao, Rakshith Sharma, Cheng Qian +5
Objective: The COVID-19 pandemic has created many challenges that need immediate attention. Various epidemiological and deep learning models have been developed to predict the COVI…
COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching
Junyi Gao, Cao Xiao, Lucas M. Glass +1
Clinical trials play important roles in drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The availability of massive electronic he…
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,…
No-regret Non-convex Online Meta-Learning
Zhenxun Zhuang, Yunlong Wang, Kezi Yu +1
The online meta-learning framework is designed for the continual lifelong learning setting. It bridges two fields: meta-learning which tries to extract prior knowledge from past ta…
Pre-training of Graph Augmented Transformers for Medication Recommendation
Junyuan Shang, Tengfei Ma, Cao Xiao +1
Medication recommendation is an important healthcare application. It is commonly formulated as a temporal prediction task. Hence, most existing works only utilize longitudinal elec…
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Shenda Hong, Cao Xiao, Tengfei Ma +2
Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities. Recently, deep learning models showed initial success on modeling ECG data, however t…
RDPD: Rich Data Helps Poor Data via Imitation
Shenda Hong, Cao Xiao, Trong Nghia Hoang +3
In many situations, we need to build and deploy separate models in related environments with different data qualities. For example, an environment with strong observation equipment…
Semiclassical approximation in stochastic optimal control I. Portfolio construction problem
Sakda Chaiworawitkul, Patrick S. Hagan, Andrew Lesniewski
This is the first in a series of papers in which we study an efficient approximation scheme for solving the Hamilton-Jacobi-Bellman equation for multi-dimensional problems in stoch…