NetDiff – Bayesian model selection for differential gene regulatory network inference
- Submitting institution
-
The University of Surrey
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9027865_1
- Type
- D - Journal article
- DOI
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10.1038/srep39224
- Title of journal
- Scientific Reports
- Article number
- 39224
- First page
- -
- Volume
- 6
- Issue
- 1
- ISSN
- 2045-2322
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2016
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novelty and importance of this paper is that it provides a significant improvement to the model selection process for biological networks. Specifically, this enables the community to identify network features unique to a specific condition with fewer false positive results. The significance of this is that when, for example, applying it to the group of neurological diseases ALS (amyotrophic lateral sclerosis) it robustly identified differential regulatory interactions between conditions. The work is timely in providing novel ways to turn existing biological experiments into actionable hypotheses that can for example identify potential ALS drug targets and direct further experimental work.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -