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

• Published in: BMC genomics Vol. 16 Suppl 1; no. S1; p. S2
• Main Authors: Swan, Anna L, Stekel, Dov J, Hodgman, Charlie, Allaway, David, Alqahtani, Mohammed H, Mobasheri, Ali, Bacardit, Jaume
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 15.01.2015; BioMed Central
• Subjects: Algorithms; Animals; Biomarkers - metabolism; Cartilage - metabolism; Databases, Genetic; Databases, Protein; Dogs; Extracellular Matrix - metabolism; Gene Expression Profiling; Heuristics; Humans; Inflammation - metabolism; Inflammation - pathology; Machine Learning; Proteomics
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-16-S1-S2

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. 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. 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.