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An Improved Systematic Approach to Predicting Transcription Factor Target Genes Using Support Vector Machine

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dc.contributor Middle Tennessee State University. School of Agribusiness and Agriscience. en_US
dc.contributor.author Cui, Song en_US
dc.contributor.author Youn, Eunseog en_US
dc.contributor.author Lee, Joohyun en_US
dc.contributor.author Maas, Stephan J. en_US
dc.contributor.author Kestler, Hans A. en_US
dc.date.accessioned 2014-06-24T15:22:35Z
dc.date.available 2014-06-24T15:22:35Z
dc.date.issued 2014-03-17 en_US
dc.identifier.citation PLoS ONE. 2014 Mar 17;9(4):e94519 en_US
dc.identifier.uri http://jewlscholar.mtsu.edu/handle/mtsu/4225
dc.description.abstract Biological prediction of transcription factor binding sites and their corresponding transcription factor target genes (TFTGs) makes great contribution to understanding the gene regulatory networks. However, these approaches are based on laborious and time-consuming biological experiments. Numerous computational approaches have shown great potential to circumvent laborious biological methods. However, the majority of these algorithms provide limited performances and fail to consider the structural property of the datasets. We proposed a refined systematic computational approach for predicting TFTGs. Based on previous work done on identifying auxin response factor target genes from Arabidopsis thaliana co-expression data, we adopted a novel reverse-complementary distance-sensitive n-gram profile algorithm. This algorithm converts each upstream sub-sequence into a high-dimensional vector data point and transforms the prediction task into a classification problem using support vector machine-based classifier. Our approach showed significant improvement compared to other computational methods based on the area under curve value of the receiver operating characteristic curve using 10-fold cross validation. In addition, in the light of the highly skewed structure of the dataset, we also evaluated other metrics and their associated curves, such as precision-recall curves and cost curves, which provided highly satisfactory results. en_US
dc.rights This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. en_US
dc.title An Improved Systematic Approach to Predicting Transcription Factor Target Genes Using Support Vector Machine en_US
dc.type Research Article en_US


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