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 language | English |
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Pages (from-to) | 138-148 |
Journal | Machine Learning & Knowledge Extraction |
Volume | 1 |
Issue number | 1 |
DOIs | |
Publication status | Published - Aug 2018 |
Keywords
- applied statistics
- biomarker
- causal networks
- experimental validation
- genomics
- machine learning
- network inference
- network science