Discovering Essential Multiple Gene Effects through Large Scale Optimization: an Application to Human Cancer Metabolism
- Submitting institution
-
Teesside University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 15839032
- Type
- D - Journal article
- DOI
-
10.1109/TCBB.2020.2973386
- Title of journal
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Article number
- 0
- First page
- -
- Volume
- 0
- Issue
- -
- ISSN
- 1545-5963
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2020
- 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
-
5
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The computational model presented in this paper provides new insights into cancer development. Cancer metabolism is not yet fully understood and this paper presents a new approach for analysing genetic effects on cancer development. The research was carried out in collaboration with Microsoft Research, Cambridge; the results of this collaboration led to the award of a research grant to investigate breast cancer metabolism using machine learning in order to develop personalised tests for earlier detection of breast cancer from Earlier.org, running 2020-2022.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -