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Bioinformatics: Trends in gene expression analysis

机译:生物信息学:基因表达分析的趋势

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摘要

Bioinformatics is an interesting combination of biology and computational sciences, which help scientists and researchers to do more biological experiments to improve the life of living being. Gene expression is fundamental biological basics of cell biology. It is also responsible for genetic as well as physical or biochemical characteristics of an organism. The study of gene expression analysis helps to predict resultant protein product, in identifying abnormal functioning of cells which may responsible for various diseases, and in designing new drugs. For analysis purpose, DNA Microarray is an important tool as number of genes can simultaneously be observed. The output of DNA Microarray is vast databases which need to be processed by computation tools to take out biological significance. Computation tools include various algorithms of data mining, pattern recognition, support vector machines etc. Vast literature is available which demonstrate different algorithms and their result for analysis purpose. To find unique algorithms which satisfies all the requisite constraints is still the research topic. In this paper, we try to discuss all the major computational tools, various major methods from last many years which are used as a trends in gene expression analysis. We also try to discusses major difficulties while applying these methods to databases for analysis purpose.
机译:生物信息学是生物学与计算科学的有趣结合,可帮助科学家和研究人员进行更多的生物学实验以改善生物的生活。基因表达是细胞生物学的基本生物学基础。它还负责生物体的遗传以及物理或生化特征。基因表达分析的研究有助于预测最终的蛋白质产物,确定可能导致多种疾病的细胞异常功能,并设计新药物。出于分析目的,DNA微阵列是一种重要的工具,因为可以同时观察到许多基因。 DNA Microarray的输出是庞大的数据库,需要使用计算工具对其进行处理以获取生物学意义。计算工具包括各种数据挖掘算法,模式识别,支持向量机等。现有大量文献证明了不同的算法及其结果以用于分析目的。寻找满足所有必要约束的独特算法仍然是研究的主题。在本文中,我们尝试讨论所有主要的计算工具,以及近几年来用作基因表达分析趋势的各种主要方法。我们还尝试讨论将这些方法应用于数据库以进行分析时遇到的主要困难。

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