Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

Frank Emmert-Streib, Matthias Dehmer

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.

Original languageEnglish
Pages (from-to)138-148
JournalMachine Learning & Knowledge Extraction
Volume1
Issue number1
DOIs
Publication statusPublished - Aug 2018

Keywords

  • applied statistics
  • biomarker
  • causal networks
  • experimental validation
  • genomics
  • machine learning
  • network inference
  • network science

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