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A Study on Contrastive Graph Neural Network Pretraining for Predicting Transcriptome Profiles

by Jiaji Ma, Scott Auerbach, Guojing Cong
Publication Type
Conference Paper
Book Title
Proceedings of the 2024 International Conference on Computational Science and Computational Intelligence (CSCI)
Publication Date
Page Numbers
1 to 14
Publisher Location
Germany
Conference Name
2024 International Conference on Computational Science and Computational Intelligence (CSCI)
Conference Location
Las Vegas, Nevada, United States of America
Conference Sponsor
Various
Conference Date
-

We study graph neural network learning for transcriptomics with limited amount of labeled data. Our study reveals that simple GNN architectures perform well and do not suffer from over-fitting as the more sophisticated ones. Our study shows that although contrastive learning as a pretraining strategy has been successful in predicting properties such as formation and binding energy, it is not effective for transcriptomics.