Overcoming sparseness of biomedical networks to identify drug repositioning candidates
algorithms, biological network, Biological systems, Compounds, computational systems biology, Diseases, drug repurposing, Drugs, Heterogeneous networks, Protein engineering, Proteins
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Modeling complex biological systems is necessary to understand biochemical interactions behind pharmacological effects of drugs. Successful in silico drug repurposing relies on 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 matrix completion to overcome the sparseness of biomedical data and to enrich the set of 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 algorithms is available at http://bioinfo.cs.uni.edu/AEONET.html.
Department of Computer Science
Original Publication Date
DOI of published version
UNI ScholarWorks, Rod Library, University of Northern Iowa
Poleksic, Aleksandar, "Overcoming sparseness of biomedical networks to identify drug repositioning candidates" (2021). Faculty Publications. 192.