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A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

Swan, AL, Stekel, DJ, Hodgman, C, Allaway, D, Algahtani, MH, Mobasheri, A and Bacardit, J (2015) A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data BMC GENOMICS, 16, ARTN S.

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Abstract

BACKGROUND: Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets. RESULTS AND DISCUSSION: Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large 'omics' datasets are increasingly being used in the area of rheumatology. CONCLUSIONS: Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences > School of Veterinary Medicine
Authors :
AuthorsEmailORCID
Swan, ALUNSPECIFIEDUNSPECIFIED
Stekel, DJUNSPECIFIEDUNSPECIFIED
Hodgman, CUNSPECIFIEDUNSPECIFIED
Allaway, DUNSPECIFIEDUNSPECIFIED
Algahtani, MHUNSPECIFIEDUNSPECIFIED
Mobasheri, AUNSPECIFIEDUNSPECIFIED
Bacardit, JUNSPECIFIEDUNSPECIFIED
Date : 15 January 2015
Identification Number : 10.1186/1471-2164-16-S1-S2
Uncontrolled Keywords : Science & Technology, Life Sciences & Biomedicine, Biotechnology & Applied Microbiology, Genetics & Heredity, RHEUMATOID-ARTHRITIS, ARTICULAR-CARTILAGE, GENE-EXPRESSION, MASS-SPECTROMETRY, SYNOVIAL TISSUES, DNA MICROARRAY, EXPLANT MODEL, OSTEOARTHRITIS, PROTEIN, IDENTIFICATION
Related URLs :
Additional Information : © 2015 Swan et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Depositing User : Symplectic Elements
Date Deposited : 07 Oct 2015 09:11
Last Modified : 07 Oct 2015 09:11
URI: http://epubs.surrey.ac.uk/id/eprint/808677

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