Modernizing Open-TGGATEs Through Data and AI Methods
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Cong, Guojing [ORNL]↗
Engineering topics
Publications and source records attributed to Auerbach, Scott [NIH].
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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.