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Found 6 papers, 4 papers with code
Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications
In this paper, we first propose a new task, which is the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task.
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records
We propose a general supervised contrastive loss $\mathcal{L}_{\text{Contrastive Cross Entropy} } + \lambda \mathcal{L}_{\text{Supervised Contrastive Regularizer}}$ for learning both binary classification (e. g. in-hospital mortality prediction) and multi-label classification (e. g. phenotyping) in a unified framework.
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research.
Visualizing Deep Graph Generative Models for Drug Discovery
Drug discovery aims at designing novel molecules with specific desired properties for clinical trials.
MoFlow: An Invertible Flow Model for Generating Molecular Graphs
Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process.
Neural Dynamics on Complex Networks
To address these challenges, we propose to combine Ordinary Differential Equation Systems (ODEs) and Graph Neural Networks (GNNs) to learn continuous-time dynamics on complex networks in a data-driven manner.
