Syndrome based on genetic data.
Journal: J Transl Med. 2009 Sep 22;7(1):81. [Epub ahead of print]
Authors: Huang LC, Hsu SY, Lin E.
NLM Citation: PMID: 19772600
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 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.
[Note: This is an Open Access article, the full text of which
can be found for free at
http://www.translational-medicine.com/content/pdf/1479-5876-7-81.pdf ]
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