Fast Dual-Regularized Autoencoder for Sparse Biological Data

• URL: http://arxiv.org/abs/2401.16664v2
• Date: Wed, 13 Mar 2024 17:03:40 GMT
• Title: Fast Dual-Regularized Autoencoder for Sparse Biological Data
• Authors: Aleksandar Poleksic
• Abstract summary: We develop a shallow autoencoder for the dual neighborhood-regularized matrix completion problem. We demonstrate the speed and accuracy advantage of our approach over the existing state-of-the-art in predicting drug-target interactions and drug-disease associations.
• Score: 65.268245109828
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Relationship inference from sparse data is an important task with applications ranging from product recommendation to drug discovery. A recently proposed linear model for sparse matrix completion has demonstrated surprising advantage in speed and accuracy over more sophisticated recommender systems algorithms. Here we extend the linear model to develop a shallow autoencoder for the dual neighborhood-regularized matrix completion problem. We demonstrate the speed and accuracy advantage of our approach over the existing state-of-the-art in predicting drug-target interactions and drug-disease associations.

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