Finding motifs using harmony search

Q2 Medicine

In Silico Biology Pub Date : 2010-02-15 DOI:10.1145/1722024.1722072

Jyotshna Dongardive, Aarti Patil, A. Bir, S. Jamkhedkar, Siby Abraham

引用次数: 8

Abstract

The paper proposes a novel methodology for finding motifs of biological data. It uses music inspired meta-heuristic optimization technique called harmony search to find motif. The model is based on randomly generated l-mers as the initial harmony memory. Pitch adjustment and random selection are used to generate new l-mers, which are adjudged by a specially defined objective function. The proposed method is experimentally validated using sequences of Human Papillomavirus strains obtained from accredited and authorized sources.

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使用和声搜索寻找主题

本文提出了一种寻找生物数据基序的新方法。它使用音乐启发的元启发式优化技术，即和声搜索来寻找母题。该模型基于随机生成的l-mer作为初始和声记忆。该算法采用音调调整和随机选择的方法生成新的l-mer，并通过一个特殊定义的目标函数对其进行判断。所提出的方法通过从认可和授权来源获得的人乳头瘤病毒株序列进行了实验验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.