Chapter 13. Methods and Challenges of Identifying Biomolecular Relationships and Networks Associated with Complex Diseases/Phenotypes, and Their Application to Drug Treatments

  1. Gil Alterovitz and
  2. Marco Ramoni
  1. Mie Rizig

Published Online: 7 JUL 2010

DOI: 10.1002/9780470669716.ch13

Knowledge-Based Bioinformatics: From Analysis to Interpretation

Knowledge-Based Bioinformatics: From Analysis to Interpretation

How to Cite

Rizig, M. (2010) Methods and Challenges of Identifying Biomolecular Relationships and Networks Associated with Complex Diseases/Phenotypes, and Their Application to Drug Treatments, in Knowledge-Based Bioinformatics: From Analysis to Interpretation (eds G. Alterovitz and M. Ramoni), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470669716.ch13

Editor Information

  1. Harvard Medical School and Massachusetts Institute of Technology, Boston, USA

Author Information

  1. Department of Mental Health, Sciences, Windeyer Institute, London, UK

Publication History

  1. Published Online: 7 JUL 2010
  2. Published Print: 16 JUL 2010

ISBN Information

Print ISBN: 9780470748312

Online ISBN: 9780470669716

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Keywords:

  • Complex traits: clinical phenomenology and molecular background;
  • Coronary Artery Disease (CAD);
  • single-input-module motif (SIM);
  • Single Nucleotide Polymorphisms (SNPs);
  • Genome-Wide Association Studies (GWAS);
  • Gene Ontology (GO);
  • expression quantitative trait loci (eQTLs);
  • Advantages of networks exploration in molecular biology and drug discovery

Summary

This chapter contains sections titled:

  • Complex traits: clinical phenomenology and molecular background

  • Why it is challenging to infer relationships between genes and phenotypes in complex traits?

  • Bottom-up or top-down: which approach is more useful in delineating complex traits key drivers?

  • High-throughput technologies and their applications in complex traits genetics

  • Integrative systems biology: a comprehensive approach to mining high-throughput data

  • Methods applying systems biology approach in the identification of functional relationships from gene expression data

  • Advantages of networks exploration in molecular biology and drug discovery

  • Practical examples of applying systems biology approaches and network exploration in the identification of functional modules and disease-causing genes in complex phenotypes/diseases

  • Challenges and future directions

  • References