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Found 16 papers, 10 papers with code
Adaptive Pseudo-labeling for Quantum Calculations
Machine learning models have recently shown promise in predicting molecular quantum chemical properties.
Machine Learning Applications for Therapeutic Tasks with Genomics Data
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 development tasks.
Graph Representation Learning in Biomedicine
Biomedical networks are universal descriptors of systems of interacting elements, from protein interactions to disease networks, all the way to healthcare systems and scientific knowledge.
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.
HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data
Next, these embeddings will be fed into the knowledge embedding module to generate knowledge embeddings that are pretrained using external knowledge on pharmaco-kinetic properties and trial risk from the web.
An Interpretable End-to-end Fine-tuning Approach for Long Clinical Text
Unstructured clinical text in EHRs contains crucial information for applications including decision support, trial matching, and retrospective research.
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics.
SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization
Furthermore, most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is a more meaningful but harder task.
Graph Meta Learning via Local Subgraphs
G-Meta learns how to quickly adapt to a new task using only a handful of nodes or edges in the new task and does so by learning from data points in other graphs or related, albeit disjoint label sets.
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks
Here, we present SkipGNN, a graph neural network approach for the prediction of molecular interactions.
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction
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 drug compound space.
DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery.
Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation
Clinical notes contain rich data, which is unexploited in predictive modeling compared to structured data.
CASTER: Predicting Drug Interactions with Chemical Substructure Representation
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality.
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Clinical notes contain information about patients that goes beyond structured data like lab values and medications.
EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection
Epistasis (gene-gene interaction) is crucial to predicting genetic disease.
