Version 1
: Received: 24 August 2024 / Approved: 26 August 2024 / Online: 27 August 2024 (12:44:15 CEST)
How to cite:
Inoue, Y.; Lee, H.; Fu, T.; Luna, A. drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network. Preprints2024, 2024081941. https://doi.org/10.20944/preprints202408.1941.v1
Inoue, Y.; Lee, H.; Fu, T.; Luna, A. drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network. Preprints 2024, 2024081941. https://doi.org/10.20944/preprints202408.1941.v1
Inoue, Y.; Lee, H.; Fu, T.; Luna, A. drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network. Preprints2024, 2024081941. https://doi.org/10.20944/preprints202408.1941.v1
APA Style
Inoue, Y., Lee, H., Fu, T., & Luna, A. (2024). drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network. Preprints. https://doi.org/10.20944/preprints202408.1941.v1
Chicago/Turabian Style
Inoue, Y., Tianfan Fu and Augustin Luna. 2024 "drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network" Preprints. https://doi.org/10.20944/preprints202408.1941.v1
Abstract
Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts. However, a major challenge in drug response (DR) prediction is model interpretability as it aids in the validation of findings. This is important in biomedicine, where models need to be understandable in comparison with established knowledge of drug interactions with proteins. drGAT, a graph deep learning model, leverages a heterogeneous graph composed of relationships between proteins, cell lines, and drugs. drGAT is designed with two objectives: DR prediction as a binary sensitivity prediction and elucidation of drug mechanism from attention coefficients. drGAT has demonstrated superior performance over existing models, achieving 78\% accuracy (and precision), and 76\% F1 score for 269 DNA-damaging compounds of the NCI60 drug response dataset. To assess the model's interpretability, we conducted a review of drug-gene co-occurrences in Pubmed abstracts in comparison to the top 5 genes with the highest attention coefficients for each drug. We also examined whether known relationships were retained in the model by inspecting the neighborhoods of topoisomerase-related drugs. For example, our model retained TOP1 as a highly weighted predictive feature for irinotecan and topotecan, in addition to other genes that could potentially be regulators of the drugs. Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients.
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
