Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology
Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine...
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| Format: | Article |
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Mary Ann Liebert
2013
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| Online Access: | https://eprints.nottingham.ac.uk/2349/ |
| _version_ | 1848790761828515840 |
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| author | Swan, Anna L. Mobasheri, Ali Allaway, David Liddell, Susan Bacardit, Jaume |
| author_facet | Swan, Anna L. Mobasheri, Ali Allaway, David Liddell, Susan Bacardit, Jaume |
| author_sort | Swan, Anna L. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes. |
| first_indexed | 2025-11-14T18:17:45Z |
| format | Article |
| id | nottingham-2349 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:17:45Z |
| publishDate | 2013 |
| publisher | Mary Ann Liebert |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-23492020-05-04T20:18:32Z https://eprints.nottingham.ac.uk/2349/ Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology Swan, Anna L. Mobasheri, Ali Allaway, David Liddell, Susan Bacardit, Jaume Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes. Mary Ann Liebert 2013-12 Article PeerReviewed Swan, Anna L., Mobasheri, Ali, Allaway, David, Liddell, Susan and Bacardit, Jaume (2013) Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology. OMICS: a Journal of Integrative Biology, 17 (12). pp. 595-610. ISSN 1536-2310 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3837439/ doi:10.1089/omi.2013.0017 doi:10.1089/omi.2013.0017 |
| spellingShingle | Swan, Anna L. Mobasheri, Ali Allaway, David Liddell, Susan Bacardit, Jaume Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology |
| title | Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology |
| title_full | Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology |
| title_fullStr | Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology |
| title_full_unstemmed | Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology |
| title_short | Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology |
| title_sort | application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology |
| url | https://eprints.nottingham.ac.uk/2349/ https://eprints.nottingham.ac.uk/2349/ https://eprints.nottingham.ac.uk/2349/ |