Data Integration and Mining for Synthetic Biology Design
- Submitting institution
-
University of Newcastle upon Tyne
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 225552-67400-1292
- 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
-
http://dx.doi.org/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)
-
B - Interdisciplinary Computing and Complex Biosystems (ICOS)
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is a landmark in the field resulting from a collaboration with Washington University in Seattle. The rational engineering of biological systems needs data to inform the design process. This paper shows how our research in data integration has been used to devise an approach for mining multiple biological sources at once, filling a gap in the technological approaches to enriching and improving the design of useful engineered biological systems.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -