Faculty Publications

Fast Dual-Regularized Autoencoder for Sparse Biological Data

Document Type

Conference

Keywords

biological relationship inference, drug repurposing, drug target interactions, recommender systems, sparse matrix completion

Journal/Book/Conference Title

EPiC Series in Computing

Volume

101

First Page

113

Last Page

121

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.

Department

Department of Computer Science

Original Publication Date

7-12-2024

DOI of published version

10.29007/v896

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