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نوع الوثيقة : مقال في مجلة دورية 
عنوان الوثيقة :
A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data
A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data
 
لغة الوثيقة : الانجليزية 
المستخلص : 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. 
ردمد : 1471-2164 
اسم الدورية : BMC Genomics 
المجلد : 16 
العدد : 1 
سنة النشر : 1436 هـ
2015 م 
نوع المقالة : مقالة علمية 
تاريخ الاضافة على الموقع : Thursday, April 28, 2016 

الباحثون

اسم الباحث (عربي)اسم الباحث (انجليزي)نوع الباحثالمرتبة العلميةالبريد الالكتروني
Anna L SwanSwan, Anna Lباحث رئيسي  
Dov J StekelStekel, Dov Jباحث مشارك  
Charlie HodgmanHodgman, Charlie باحث مشارك  
David AllawayAllaway, David باحث مشارك  
Mohammed H AlqahtaniAlqahtani, Mohammed Hباحث مشارك  
Ali MobasheriMobasheri, Ali باحث مشارك  
Jaume BacarditBacardit, Jaume باحث مشارك  

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