Complex Biological Data Mining and Knowledge Discovery

Fatima Kabli
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Abstract

The mass of data available on the Internet is rapidly increasing; the complexity of this data is discussed at the level of the multiplicity of information sources, formats, modals, and versions. Facing the complexity of biological data, such as the DNA sequences, protein sequences, and protein structures, the biologist cannot simply use the traditional techniques to analyze this type of data. The knowledge extraction process with data mining methods for the analysis and processing of biological complex data is considered a real scientific challenge in the search for systematically potential relationships without prior knowledge of the nature of these relationships. In this chapter, the authors discuss the Knowledge Discovery in Databases process (KDD) from the Biological Data. They specifically present a state of the art of the best known and most effective methods of data mining for analysis of the biological data and problems of bioinformatics related to data mining.
复杂生物数据挖掘与知识发现
互联网上可用的数据量正在迅速增加;这些数据的复杂性是在信息源、格式、模态和版本的多样性级别上讨论的。面对复杂的生物数据,如DNA序列、蛋白质序列和蛋白质结构,生物学家不能简单地使用传统的技术来分析这类数据。利用数据挖掘方法对生物复杂数据进行分析和处理的知识提取过程被认为是一个真正的科学挑战,因为它需要在没有事先了解这些关系性质的情况下,系统性地寻找潜在的关系。在本章中,作者讨论了基于生物数据的数据库知识发现过程(KDD)。他们特别介绍了最著名和最有效的数据挖掘方法,用于分析生物数据和与数据挖掘相关的生物信息学问题。
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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