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|Title:||Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification||Authors:||Zhou, XD
Multifactor dimensionality reduction
|Issue Date:||2018||Publisher:||BioMed Central||Source:||BMC bioinformatics, Sept. 2018, v. 19, 329, p. 1-13 How to cite?||Journal:||BMC bioinformaticsonline only||Abstract:||Background: Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multifactor Dimensionality Reduction (MDR) is originally proposed to identify gene-gene and gene- environment interactions associated with binary status of complex diseases. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these and other
Methods are still not computationally efficient or effective.
Results: Generalized Fuzzy Quantitative trait MDR (GFQMDR) is proposed in this paper to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then selecting best sets of genetic markers, mainly single nucleotide polymorphisms (SNPs) or simple sequence length polymorphic markers (SSLPs), as having strong association with the trait through generalized fuzzy classification using extended member functions. Experimental results on simulated datasets and real datasets show that our algorithm has better success rate, classification accuracy and consistency in identifying gene-gene interactions associated with QTs.
Conclusion: The proposed algorithm provides a more effective way to identify gene-gene interactions associated with quantitative traits.
|URI:||http://hdl.handle.net/10397/80247||EISSN:||1471-2105||DOI:||10.1186/s12859-018-2361-5||Rights:||© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
The following publication Zhou, X.D., & Chan, K.C.C. (2018). Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification. BMC bioinformatics, 19, 329, 1-13 is available at https://dx.doi.org/10.1186/s12859-018-2361-5
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