Identifying human miRNA targets with a genetic algorithm

Q2 Medicine

In Silico Biology Pub Date : 2010-02-15 DOI:10.1145/1722024.1722059

Kalle Karhu, S. Khuri, Juho Mäkinen, J. Tarhio

引用次数: 3

Abstract

MicroRNAs (miRNAs) play an important role in eukaryotic gene regulation. Although thousands of miRNAs have been identified in laboratories around the world, most of their targets still remain unknown. Different computational techniques exist to predict miRNA targets. In this article, we propose a new method for identifying human miRNA-mRNA interactions based on a genetic algorithm. Our cross-validation results indicate that the genetic algorithm-based miRNA target predictor outperforms the MiRanda package as evidenced by high true positive rates and moderate false positive rates.

查看原文本刊更多论文

用遗传算法鉴定人类miRNA靶点

MicroRNAs (miRNAs)在真核生物基因调控中发挥着重要作用。尽管世界各地的实验室已经发现了数千种mirna，但它们的大多数靶标仍然未知。存在不同的计算技术来预测miRNA靶标。在本文中，我们提出了一种基于遗传算法识别人类miRNA-mRNA相互作用的新方法。我们的交叉验证结果表明，基于遗传算法的miRNA目标预测器优于MiRanda包，这证明了高真阳性率和中等假阳性率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

In Silico Biology Computer Science-Computational Theory and Mathematics

CiteScore

2.20

自引率

0.00%

发文量

期刊介绍： The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.