tailieunhanh - báo cáo hóa học:" A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data"

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí sinh học quốc tế đề tài : A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data | Journal of Translational Medicine BioMed Central Research A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data Lung-Cheng Huang41 2 Sen-Yen Hsu43 and Eugene Lin 4 Open Access Address Department of Psychiatry National Taiwan University Hospital Yun-Lin Branch Taiwan 2Graduate Institute of Medicine Kaohsiung Medical University Kaohsiung Taiwan 3Department of Psychiatry Chi Mei Medical Center Liouying Tainan Taiwan and 4Vita Genomics Inc 7 Fl No 6 Sec 1 Jung-Shing Road Wugu Shiang Taipei Taiwan Email Lung-Cheng Huang - psychidr@ Sen-Yen Hsu - 779002@ Eugene Lin - Corresponding author fEqual contributors Published 22 September 2009 Received 23 June 2009 J n I I 11 m rm I Accepted 22 September 2009 Journal of Translational Medicine 2009 7 81 doi 1479-5876-7-81 This article is available from http content 7 1 81 2009 Huang et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background In the studies of genomics it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome CFS using genetic factors such as single nucleotide polymorphisms SNPs . Methods We employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. To uncover relationships between CFS and SNPs we applied three classification algorithms including naive Bayes the support vector machine algorithm and the decision tree algorithm. Furthermore we .