Outlier detection at the transcriptome-proteome interface
- Submitting institution
-
University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 20753012
- Type
- D - Journal article
- DOI
-
10.1093/bioinformatics/btv182
- Title of journal
- Bioinformatics
- Article number
- -
- First page
- 2530
- Volume
- 31
- Issue
- 15
- ISSN
- 1367-4803
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2015
- 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
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work, deriving a novel algorithm for detecting outliers in a regression setting to find proteins whose concentrations are post-translationally regulated (i.e. after they are made), resulted in a computational methodology that solves a biological problem. Subsequent work showed its effectiveness in regulating protein concentrations along the cell cycle: https://tinyurl.com/qru9nsv and led to experimental validations that identified molecular mechanisms: https://tinyurl.com/w9hxxz2 The algorithm had impact in other domains too, as evidenced by a successful bid to Innovate UK in partnership with The ai Corporation (https://www.aicorporation.com/) to refine and integrate the algorithm into the company’s products on detecting fraudulent transactions (https://tinyurl.com/rym4uqw).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -