GraphProt: Modeling binding preferences of RNA-binding proteins
- Submitting institution
-
University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1789
- Type
- D - Journal article
- DOI
-
10.1186/gb-2014-15-1-r17
- Title of journal
- Genome Biology
- Article number
- ARTN R17
- First page
- -
- Volume
- 15
- Issue
- 1
- ISSN
- 1474-7596
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 118
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Here we introduce a state-of-the-art computational framework for learning both sequence and structure-binding preferences of proteins binding to RNA based on an efficient graph kernel approach. RNA-binding proteins (RBPs) regulate several processes in human cells and the disruption of the interactions that they mediate cause many diseases. However experimental techniques to identify these interactions are affected by noise which we compensate with the proposed robust in-silico modelling. The application of our tool has revealed unexpected binding ability of several RBPs to lncRNAs that can be used to categorize subtypes of breast tumours in a reliable way (10.1093/bib/bbv031).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -