生物信息学中数据挖掘方法综述

A. Mabu, R. Prasad, Raghav Yadav, S. Jauro
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引用次数: 3

摘要

生物信息学是指生物化学和生物学数据的收集、分类、存储和审查。它特别利用个人计算机,作为分子遗传学和基因组学的实施。它是一个迅速崛起的科学分支,是跨学科的,利用基础科学和语言学的策略和思想。本文首先对当前和下一代测序技术进行了综述,并指出了其数据分析能力存在的问题。我们介绍了当前的生物信息学方法和基于预测的数据挖掘算法的熟练程度。支持生物信息学分析的基本规则已经被授予。基于对主要分析工具的估计,我们展示了适用于特定研究任务的各种检查工具分类的各种数据挖掘算法的概述。我们还分析了大规模数据挖掘的困难,此外,生物信息学领域的管理,并评估了许多算法的性能,基于观察它们在不同论文中产生的错误率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review of Data Mining Methods in Bioinformatics
Bioinformatics refers to the collection, classification, storage and the scrutiny of biochemical and biological data. It utilizes personal computers especially, as implemented toward molecular genetics and genomics. It is a quickly emerging division of science and is exceedingly interdisciplinary, utilizing strategies and ideas from basic science and linguistics. This paper, initially display a review of the current and next generation sequencing (NGS) technologies and pointed out some problems regarding its data analysis capability. We present the current bioinformatics methods and proficiency of the prediction based data mining algorithms. The fundamental rule that support bioinformatics analysis has been conferred. Based on the estimation of the chief analysis instruments, we have displayed the overview of various data mining algorithms for the assortment of various examination tools applicable in particular research errands. We also analyze the difficulties in extensive scale data mining, furthermore, administration in the arena of bioinformatics and assessed numerous algorithms' performance grounded on watching error rate they yield in different papers.
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