Search
Now showing items 1-5 of 5
Effective Sequence Models and Graph Neural Networks for Molecular Data Analysis
(2022-08-16)
Drug discovery is the process of discovering new candidate medications. New drugs are continually developed by pharmaceutical industries to address increasing medical needs. Drug discovery involves a series of processes ...
Using ChebConv and B-Spline GNN models for Solving Unit Commitment and Economic Dispatch in a day ahead Energy Trading Market based on ERCOT Nodal Model
(2020-05-22)
Spectral Convolutions and B-Spline Graph Neural Network techniques have been used in past to learn embeddings in various complex, multidimensional structured knowledge graphs like genetics, social networks, geometric shapes ...
Using Graph Convolutional Network and Message Passing Neural Networks for Solving Unit Commitment and Economic Dispatch in a day ahead Energy Trading Market based on ERCOT Nodal Model.
(2020-05-22)
Various machine learning applications will pre-process graphical representations into a vector of real values which in turn loses information regarding graph structure. Graph Neural Networks (GNNs) are a combination of an ...
Graph Representation Learning for Heterogeneous Multimodal Biomedical Data
(2022-12-20)
The emergence of high-throughput sequencing technology has generated a wealth of “multi-omics” data, capturing information about different types of biomolecules at multiple levels. Since large-scale genomics, transcriptomics, ...
DEEP LEARNING FOR MOLECULAR PROPERTY PREDICTION
(2023-08-14)
Drug discovery has always been a crucial task for society, and molecular property prediction is one of the fundamental problem. It is responsible for identifying the target properties or severe side-effects, so that certain ...