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Prediction of Insertion-Site Preferences of Transposons Using Support Vector Machines and Artificial Neural Networks

机译:支持向量机和人工神经网络预测转座子的插入位点偏好

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摘要

Transposons are segments of DNA that are capable of moving from one location to another within the genome of a cell. Understanding transposon insertion-site preferences is critically important in functional genomics and gene therapy studies. It has been found that the deformability property of the local DNA structure of the integration sites, called V_(step), is of significant importance in the target-site selection process. We considered the V_(step) profiles of insertion sites and developed predictors based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM), and trained them with a Sleeping Beauty transposon dataset. We found that both ANN and SVM predictors are excellent in finding the most preferred regions. However, the SVM predictor outperforms the ANN predictor in recognizing preferred sites, in general.
机译:转座子是能够在细胞基因组中从一个位置移动到另一位置的DNA片段。在功能基因组学和基因治疗研究中,了解转座子插入位点的偏好至关重要。已经发现,整合位点的局部DNA结构的可变形性称为V_(步骤),在靶位点选择过程中具有重要意义。我们考虑了插入位点的V_(步)轮廓,并基于人工神经网络(ANN)和支持向量机(SVM)开发了预测变量,并使用Sleeping Beauty转座子数据集对其进行了训练。我们发现,ANN和SVM预测器在找到最喜欢的区域方面都很出色。但是,在识别首选站点时,一般来说,SVM预测器的性能要优于ANN预测器。

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  • 会议地点 Montreal(CA)
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    Bioinformatics Lab, Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;

    Bioinformatics Lab, Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;

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