Output details
11 - Computer Science and Informatics
University of Warwick
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Output 0 of 0 in the submission
Article title
Enhancement of plant metabolite fingerprinting by machine learning
Type
D - Journal article
Title of journal
Plant Physiology
Article number
-
Volume number
153
Issue number
4
First page of article
1506
ISSN of journal
0032-0889
Year of publication
2010
Number of additional authors
17
Additional information
<28> Published in one of the leading plant science journals (among the top 100 journals according to DBIO), this paper established machine learning, and random forests in particular, as a novel and effective method for analysing metabolomics data (Bais et al. 2012, Nucleic Acids Research). This research, cited in O’Connell’s (LipoScience Inc) “Recent advances in metabolomics in oncology”, has found application in agriculture (Nadella, ICAR New Delhi), data-mining (Bais, Iowa State), investigating environmental changes in organisms (Rivas-Ubach, Barcelona) and human disease diagnosis (Emwas, King Abdullah U; Salek, MRC Cambridge).
Interdisciplinary
-
Cross-referral requested
-
Research group
M - Methodologies and Applications
Citation count
9
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
