The rapid accumulation of data in genomics prompts us to utilize gene expression data available in public databases for predicting and repurposing drugs. Integrative computational methods that mine these data are fast, cheap, and can complement traditional methods of drug discovery by using complementary information in these distinct resources so they can be used to, potentially, develop novel cancer therapeutics. Several promising attempts have been made for predicting disease drugs, e.g., for cancer, using a large collection of gene expression data.
The findings show how we developed a systems pharmacogenomics approach and applied it to data from the Library of Integrative Network-based Cellular Signatures (LINCS) repository. As a result, we constructed Drug Association Networks summarizing perturbagens systematically with respect to their therapeutic effects. We showed that the modular structure of the DANs represent enriched Anatomical Therapeutic Chemical Classification (ATC) classes thus integrating the drug induced changes on the genotype states of the cells. We believe using such informative dataset and approach we proposed, will advance and accelerate the translation of drug discovery, development, and delivery through comprehensive Big Data analysis and health informatics for better personalized and predictive health care.
The doctoral dissertation of Msc Aliyu Musa in the field of Computing and Electrical Engineering titled Network-Based Systems Pharmacogenomics: Methods and Applications will be publicly examined in the Faculty of Faculty of Information Technology and Communication Sciences at Tampere University at 12 o’clock on Friday 2.10.2020. The Opponent will be Professor Marc-Thorsten Hütt from Jacobs University, Germany. The Custos will be Assoc. Prof. Frank Emmert-Streib from Tampere University, Finland.
The event can be followed via remote connection.
The dissertation is available online at the https://tuni.zoom.us/j/69881356485