Data Integration and Mining for Synthetic Biology Design
- Submitting institution
-
University of Keele
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 346
- Type
- D - Journal article
- DOI
-
10.1021/acssynbio.5b00295
- Title of journal
- ACS Synthetic Biology
- Article number
- -
- First page
- 1086
- Volume
- 5
- Issue
- 10
- ISSN
- 2161-5063
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2016
- URL
-
https://pubs.acs.org/doi/full/10.1021/acssynbio.5b00295
- 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
-
6
- Research group(s)
-
-
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- One of several research directions that extend and generalise from earlier EPSRC-funded synthetic biology programmes (e.g. EP/J02175X/1, see Nature Biotech paper: https://doi.org/f56pqg), this is the first use of an ontological approach to data mining in synthetic biology. The approach generalises to any standard ontology and compliant knowledge base (e.g. https://doi.org/ff5v). Subsequently, this research underpins the development and use of sythetic biology design workflows by Misirli and others (e.g. https://doi.org/ff7z).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -