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Overcoming sparseness of biomedical networks to identify drug repositioning candidates

View ORCID ProfileAleksandar Poleksic
doi: https://doi.org/10.1101/2020.06.07.138966
Aleksandar Poleksic
Department of Computer Science, University of Northern Iowa, Cedar Falls, IA 50613
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  • For correspondence: poleksic@cs.uni.edu
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Abstract

Modeling complex biological systems is necessary to understand biochemical interactions behind pharmacological effects of drugs. Successful in silico drug repurposing requires a thorough exploration of diverse biochemical concepts and their relationships, including drug’s adverse reactions, drug targets, disease symptoms, as well as disease associated genes and their pathways, to name a few. We present a computational method for inferring drug-disease associations from complex but incomplete and biased biological networks. Our method employs the compressed sensing technique to overcome the sparseness of biomedical data and, in turn, to enrich the set of verified relationships between different biomedical entities. We present a strategy for identifying network paths supportive of drug efficacy as well as a computational procedure capable of combining different network patterns to better distinguish treatments from non-treatments. The data and programs are freely available at http://bioinfo.cs.uni.edu/AEONET.html.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • http://bioinfo.cs.uni.edu/AEONET.html

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 08, 2020.
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Overcoming sparseness of biomedical networks to identify drug repositioning candidates
Aleksandar Poleksic
bioRxiv 2020.06.07.138966; doi: https://doi.org/10.1101/2020.06.07.138966
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Overcoming sparseness of biomedical networks to identify drug repositioning candidates
Aleksandar Poleksic
bioRxiv 2020.06.07.138966; doi: https://doi.org/10.1101/2020.06.07.138966

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