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

  • Tampere University
  • Private University for Health Sciences, Medical Informatics and Technology
  • Nankai University
  • Upper Austria University of Applied Sciences

Research output: Contribution to journalArticlepeer-review

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
JournalMachine Learning and Knowledge Extraction
Volume1
Issue number1
DOIs
Publication statusPublished - 22 Dec 2019
Externally publishedYes

Keywords

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

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