Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics
Selvaraj, Devipriya
Unknown Affiliation
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Bulletin of Electrical Engineering and Informatics

 1 
Improved half-maximal inhibitory concentration regression model using amyotrophic lateral sclerosis data Selvaraj, Devipriya; M S, Vijaya; Sakkarapani, Krishnaveni
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8520

Abstract

The current research addresses the critical need for precise half-maximal inhibitory concentration regression in the neurodegenerative condition amyotrophic lateral sclerosis (ALS). Unavailable drug-induced gene expressions and irrelevant molecular descriptors have yielded regression models with less accuracy using traditional machine learning (ML). Drugs can be converted to graph format and integrated with gene expressions to learn drug-gene interactions better thereby producing precise half-maximal inhibitory concentration regression models. To accomplish this, three variants of graph neural networks (GNN) namely graph attention networks (GAT), message passing neural networks, and graph isomorphism networks are utilized in the proposed work. The gene expression profiles of ALS drugrelated genes were retrieved from the DepMap PRISM drug repurposing hub, and the drug graphs with their accompanying half-maximal inhibitory concentration values were obtained from the ChEMBL databases. The graph is constructed for ninety approved drugs connected to 32 key protein targets of ALS and its related conditions. The half-maximal inhibitory concentration regression model trained with optimized hyperparameters in GAT performs well with an R2 score of 0.92, a mean absolute error (MAE) of 0.20, and a root mean square error (RMSE) of 0.17. This model produced better results than other ML and deep learning models.
Co-Authors M S, Vijaya Sakkarapani, Krishnaveni