基于通路的微阵列分析用于定义统计学上显著的表型相关通路:对常用方法的回顾

M. F. Misman, S. Deris, S. Hashim, R. Jumali, M. S. Mohamad
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引用次数: 11

摘要

在这篇综述中,我们讨论了基于通路的微阵列分析方法。通常,在基于路径的分析中有两种方法:浓缩分数和监督机器学习。这些基于途径的方法通常旨在统计定义与感兴趣表型相关的重要途径。本文首先综述了基于信号通路的微阵列分析及其信号通路评分的一般流程,两种方法的应用方法,基于现有研究的优势和局限性,以及信号通路分析中使用的信号通路数据库。本文综述的目的是为了更好地了解基于通路的微阵列分析及其方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pathway-Based Microarray Analysis for Defining Statistical Significant Phenotype-Related Pathways: A Review of Common Approaches
In this review, we have discussed about approaches in pathway based microarray analysis. Commonly, there are two approaches in pathway based analysis, Enrichment  Score and Supervised Machine Learning. These pathway based approaches usually aim to statistically define significant pathways that related to phenotypes of interest. Firstly we discussed an overview of pathway based microarray analysis and its general flow processes in scoring the pathways, the methods applied in both approaches, advantages and limitations  based on current researches, and pathways database used in pathway analysis. This review aim to provide better understanding about pathway based microarray analysis and its approaches.
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