کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7682679 | 1495818 | 2013 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Selective of informative metabolites using random forests based on model population analysis
ترجمه فارسی عنوان
انتخاب متابولیت های اطلاعاتی با استفاده از جنگل های تصادفی بر اساس تجزیه و تحلیل جمعیت مدل
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی
One of the main goals of metabolomics studies is to discover informative metabolites or biomarkers, which may be used to diagnose diseases and to find out pathology. Sophisticated feature selection approaches are required to extract the information hidden in such complex 'omics' data. In this study, it is proposed a new and robust selective method by combining random forests (RF) with model population analysis (MPA), for selecting informative metabolites from three metabolomic datasets. According to the contribution to the classification accuracy, the metabolites were classified into three kinds: informative, no-informative, and interfering metabolites. Based on the proposed method, some informative metabolites were selected for three datasets; further analyses of these metabolites between healthy and diseased groups were then performed, showing by T-test that the P values for all these selected metabolites were lower than 0.05. Moreover, the informative metabolites identified by the current method were demonstrated to be correlated with the clinical outcome under investigation. The source codes of MPA-RF in Matlab can be freely downloaded from http://code.google.com/p/my-research-list/downloads/list
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Talanta - Volume 117, 15 December 2013, Pages 549-555
Journal: Talanta - Volume 117, 15 December 2013, Pages 549-555
نویسندگان
Jian-Hua Huang, Jun Yan, Qing-Hua Wu, Miguel Duarte Ferro, Lun-Zhao Yi, Hong-Mei Lu, Qing-Song Xu, Yi-Zeng Liang,