A community effort to assess and improve drug sensitivity prediction algorithms
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 173952918
- Type
- D - Journal article
- DOI
-
10.1038/nbt.2877
- Title of journal
- Nature biotechnology
- Article number
- -
- First page
- 1202
- Volume
- 32
- Issue
- 12
- ISSN
- 1087-0156
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- 5 - Biological Sciences
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
21
- Research group(s)
-
A - Computer Science
- Citation count
- 318
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This is the primary publication from challenge 1 of the NCI-DREAM Drug Sensitivity Prediction Challenge 2012.
The paper presents the winning algorithm (Bayesian multi-task multi-kernel learning, designed by Kaski) and a detailed evaluation of its performance against 44 competitors in the challenge.
The work inspired extensions - published in major forums including: Journal of Chemical Information and Modeling, 54:2347-2359, 2014; IEEE Transactions in Pattern Analysis and Machine Intelligence, 36:2047-2060, 2014."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
