Abstract
BackgroundIdentifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective.MethodsIn this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes.ResultsWe applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib).ConclusionsOur deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.
Highlights
Identifying novel therapeutic targets is crucial for the successful development of drugs
To infer potentially novel target genes, we proposed a computational framework based on a representative network embedding method that employs a deep autoencoder to map a genome-wide protein interaction network onto low-dimensional representations
Network embedding: deep autoencoder-based dimensional reduction of protein interaction network (PIN) We obtained the directed human PIN from [23]; this PIN is composed of 6338 genes and 34,814 interactions
Summary
Identifying novel therapeutic targets is crucial for the successful development of drugs. It is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. The complex, non-linear, multi-dimensional nature of big (2021) 13:92 prioritize candidate targets and repositionable drugs for candidate targets) from big data volumes. “Big Data” in the biomedical domain are generally associated with high dimensionality. Their dimensionality should be reduced to avoid undesired properties of highdimensional space, such as the curse of dimensionality [5]. Classical dimensional reduction techniques (e.g., PCA) are generally linear techniques and insufficient to handle non-linear data [4, 6]
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