miTarget: microRNA target gene prediction using a support vector machine.
miTarget: microRNA target gene prediction using a support vector machine.
复制标题
DOI：
10.1186/1471-2105-7-411
复制
发表时间：
2006-09-18
影响因子：
3
通讯作者：
Zhang BT
中科院分区：
文献类型：
--
作者：
Kim SK;Nam JW;Rhee JK;Lee WJ;Zhang BT
microRNAs（miRNAs）是一类非编码小分子RNA，在转录后调控中发挥重要作用。动物miRNA的功能通常基于其5'组分的互补性。虽然已经提出了几种计算miRNA靶基因预测方法，但它们在揭示实际靶基因方面仍然存在局限性。我们实现了miTarget，一个用于miRNA靶基因预测的支持向量机（SVM）分类器。它使用径向基函数核作为SVM特征的相似性度量，分类为结构，热力学和基于位置的特征。后者的特点是在这项研究中首次介绍，并反映了miRNA的结合机制。与以前的工具相比，SVM分类器产生高性能的生物相关的数据集从文献中获得。我们使用基因本体（GO）分析预测了人类miR-1、miR-124 a和miR-373的重要功能，并从特征选择实验中揭示了miRNA 5'区域中位置4、5和6配对的重要性。我们还为该程序提供了一个Web界面。miTarget是一种可靠的miRNA靶基因预测工具，是SVM分类器的成功应用。与以前的工具相比，它的预测是有意义的GO分析，它的性能可以提高更多的训练样本。
MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes. We implemented miTarget, a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features. The latter features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set obtained from the literature, compared with previous tools. We predicted significant functions for human miR-1, miR-124a, and miR-373 using Gene Ontology (GO) analysis and revealed the importance of pairing at positions 4, 5, and 6 in the 5' region of a miRNA from a feature selection experiment. We also provide a web interface for the program. miTarget is a reliable miRNA target gene prediction tool and is a successful application of an SVM classifier. Compared with previous tools, its predictions are meaningful by GO analysis and its performance can be improved given more training examples.
登录
查看更多内容
影响因子：
56.9
作者：
Giraldez, AJ;Mishima, Y;Schier, AF
通讯作者：
Schier, AF
影响因子：
10.5
作者：
Lai, EC;Tam, B;Rubin, GM
通讯作者：
Rubin, GM
影响因子：
64.5
作者：
Lewis, BP;Shih, IH;Burge, CB
通讯作者：
Burge, CB
影响因子：
14.9
作者：
Altuvia Y;Landgraf P;Lithwick G;Elefant N;Pfeffer S;Aravin A;Brownstein MJ;Tuschl T;Margalit H
通讯作者：
Margalit H
影响因子：
16
作者：
Jones-Rhoades, MW;Bartel, DP
通讯作者：
Bartel, DP