RAMClust : A novel feature clustering method enables spectral-matching-based annotation for metabolomics data
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11634
- Type
- D - Journal article
- DOI
-
10.1021/ac501530d
- Title of journal
- Analytical Chemistry
- Article number
- -
- First page
- 6812
- Volume
- 86
- Issue
- 14
- ISSN
- 0003-2700
- 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
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
A - Applied Computing
- Citation count
- 109
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in the top journal in analytical chemistry, this widely cited paper was one of the first on machine learning for metabolomics data. Addressing the fundamental problem of mass-spectral signature annotation, it opened a new direction in metabolomics research (e.g. Uppal et al., Chemical Research in Toxicology 2016; Peisl et al., Analytica Chimica Acta 2018). This work has also been used for making new scientific discoveries in relation to metabolomic composition of different biological samples (plant waxes, mosquitoes, ovine uterine flushings, pulmonary disease, etc.), and led to development of the influential metaRbolomics software (Stanstrup et al., Metabolites 2019).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -