Next Article in Journal
A Machine Learning Perspective on Personalized Medicine: An Automized, Comprehensive Knowledge Base with Ontology for Pattern Recognition
Previous Article in Journal
Phi-Delta-Diagrams: Software Implementation of a Visual Tool for Assessing Classifier and Feature Performance
Article Menu

Export Article

Open AccessPerspective
Mach. Learn. Knowl. Extr. 2018, 1(1), 138-148; https://doi.org/10.3390/make1010008

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

1
Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology, 33720 Tampere, Finland
2
Institute of Biosciences and Medical Technology, 33520 Tampere, Finland
3
Department of Mechatronics and Biomedical Computer Science, University for Health Sciences, Medical Informatics and Technology (UMIT), 6060 Hall in Tyrol, Austria
4
College of Computer and Control Engineering, Nankai University, Tianjin 300071, China
5
Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr Campus, 4400 Steyr, Austria
*
Author to whom correspondence should be addressed.
Received: 27 July 2018 / Revised: 17 August 2018 / Accepted: 20 August 2018 / Published: 22 August 2018
(This article belongs to the Section Network)
Full-Text   |   PDF [623 KB, uploaded 22 August 2018]   |  

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. View Full-Text
Keywords: machine learning; network inference; genomics; network science; experimental validation; biomarker; causal networks; applied statistics machine learning; network inference; genomics; network science; experimental validation; biomarker; causal networks; applied statistics
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Emmert-Streib, F.; Dehmer, M. Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations? Mach. Learn. Knowl. Extr. 2018, 1, 138-148.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Mach. Learn. Knowl. Extr. EISSN 2504-4990 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top