A computational platform and guide for acceleration of novel medicines and personalized medicine

Ioannis N. Melas, Theodore Sakellaropoulos, Junguk Hur, Dimitris Messinis, Ellen Y. Guo, Leonidas G. Alexopoulos, Jane P.F. Bai

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In the era of big data and informatics, computational integration of data across the hierarchical structures of human biology enables discovery of new druggable targets of disease and new mode of action of a drug. We present herein a computational framework and guide of integrating drug targets, gene expression data, transcription factors, and prior knowledge of protein interactions to computationally construct the signaling network (mode of action) of a drug. In a similar manner, a disease network is constructed using its disease targets. And then, drug candidates are computationally prioritized by computationally ranking the closeness between a disease network and a drug’s signaling network. Furthermore, we describe the use of the most perturbed HLA genes to assess the safety risk for immune-mediated adverse reactions such as Stevens-Johnson syndrome/toxic epidermal necrolysis.

Original languageEnglish (US)
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages181-198
Number of pages18
DOIs
StatePublished - Jan 1 2019

Publication series

NameMethods in Molecular Biology
Volume1939
ISSN (Print)1064-3745

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Keywords

  • Drug targets
  • Gene expression
  • HLA genes
  • Integer linear programming
  • Network protein interactions

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

Cite this

Melas, I. N., Sakellaropoulos, T., Hur, J., Messinis, D., Guo, E. Y., Alexopoulos, L. G., & Bai, J. P. F. (2019). A computational platform and guide for acceleration of novel medicines and personalized medicine. In Methods in Molecular Biology (pp. 181-198). (Methods in Molecular Biology; Vol. 1939). Humana Press Inc.. https://doi.org/10.1007/978-1-4939-9089-4_10