Analyzing Large Biological Datasets with an Improved Algorithm for MIC

IF 0.2 4区生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

International Journal of Data Mining and Bioinformatics Pub Date : 2014-03-14 DOI:10.1504/IJDMB.2015.071548

Shuliang Wang, Yiping Zhao

引用次数: 10

Abstract

The computational framework used the traditional similarity measures to find out the significant relationships in biological annotations. But its prerequisites that the biological annotations do not cooccur with each other is particular. To overcome it, in this paper a new method Improved Algorithm for Maximal Information Coefficient (IAMIC) is suggested to discover the hidden regularities between biological annotations. IAMIC approximates a novel similarity coefficient on maximal information coefficient with generality and equitability, by bettering axis partition through quadratic optimisation instead of violence search. The experimental results show that IAMIC is more appropriate for identifying the associations between biological annotations, and further extracting the novel associations hidden in collected data sets than other similarity measures.

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基于改进MIC算法的大型生物数据集分析

计算框架使用传统的相似性度量来找出生物注释中的重要关系。但其先决条件是生物注释不能相互发生。为了克服这一问题，本文提出了一种新的方法——改进的最大信息系数算法(IAMIC)来发现生物注释之间隐藏的规律。IAMIC通过二次优化代替暴力搜索，在最大信息系数上近似出一种新的具有通用性和公平性的相似性系数。实验结果表明，IAMIC比其他相似度度量更适合识别生物注释之间的关联，并进一步提取隐藏在收集数据集中的新关联。

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

International Journal of Data Mining and Bioinformatics 生物-数学与计算生物学

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.