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Framework for the quantitative weight-of-evidence analysis of ‘omics data for regulatory purposes
- Source :
- Regulatory Toxicology and Pharmacology. 91:S46-S60
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- A framework for the quantitative weight-of-evidence (QWoE) analysis of 'omics data for regulatory purposes is presented. The QWoE framework encompasses seven steps to evaluate 'omics data (also together with non-'omics data): (1) Hypothesis formulation, identification and weighting of lines of evidence (LoEs). LoEs conjoin different (types of) studies that are used to critically test the hypothesis. As an essential component of the QWoE framework, step 1 includes the development of templates for scoring sheets that predefine scoring criteria with scores of 0-4 to enable a quantitative determination of study quality and data relevance; (2) literature searches and categorisation of studies into the pre-defined LoEs; (3) and (4) quantitative assessment of study quality and data relevance using the respective pre-defined scoring sheets for each study; (5) evaluation of LoE-specific strength of evidence based upon the study quality and study relevance scores of the studies conjoined in the respective LoE; (6) integration of the strength of evidence from the individual LoEs to determine the overall strength of evidence; (7) characterisation of uncertainties and conclusion on the QWoE. To put the QWoE framework in practice, case studies are recommended to confirm the relevance of its different steps, or to adapt them as necessary.
- Subjects :
- 0301 basic medicine
Computer science
Statistics as Topic
010501 environmental sciences
Toxicology
computer.software_genre
Risk Assessment
01 natural sciences
Omics data
03 medical and health sciences
Strength of evidence
Component (UML)
Animals
Humans
Relevance (information retrieval)
0105 earth and related environmental sciences
Weight of evidence
Study quality
business.industry
Genomics
General Medicine
Weighting
Identification (information)
030104 developmental biology
Artificial intelligence
Data mining
business
computer
Natural language processing
Subjects
Details
- ISSN :
- 02732300
- Volume :
- 91
- Database :
- OpenAIRE
- Journal :
- Regulatory Toxicology and Pharmacology
- Accession number :
- edsair.doi.dedup.....5ef1274bdffc622c9fec01a54868d8f5
- Full Text :
- https://doi.org/10.1016/j.yrtph.2017.10.010