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A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data – Source: Journal of Translational Medicine, Sep 22, 2009

  [ 42 votes ]   [ Discuss This Article ]
By LC Huang, et al. • • September 24, 2009

[Note: This free access article explains a comparison of techniques for determining the genes most predictive of ME/CFS, so that researchers can focus on the most promising candidates for diagnostic & therapy development. To read the full text of this 36-page article, click here.]

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). [A “snip” is a variation at a single site in DNA. SNP array analysis is used primarily to determine disease susceptibility and to measure the efficacy of drug therapies specifically for the individual.]

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 C4.5 decision tree algorithm.

Furthermore, we utilized feature selection methods to identify a subset of influential SNPs. One was the hybrid feature selection approach combining the chi-squared and information-gain methods. The other was the wrapper-based feature selection method.

Results: The naive Bayes model with the wrapper-based approach performed maximally among predictive models to infer the disease susceptibility dealing with the complex relationship between CFS and SNPs.

Conclusion: We demonstrated that our approach is a promising method to assess the associations between CFS and SNPs.

Source: Journal of Translational Medicine, Sep 22, 2009;7(1):81. PMID 19772600, by Huang LC, Hsu SY, Lin E. Department of Psychiatry, National Taiwan University Hospital Yun-Lin Branch; Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung; Department of Psychiatry, Chi Mei Medical Center, Liouying, Tainan; Vita Genomics, Inc., Taipei, Taiwan. [E-mail:]

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