Browsing by Subject "Graph neural networks"
Now showing items 1-5 of 5
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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 ... -
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 ... -
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, ... -
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 ...