Krishnappa Raghavendra
Life Science/Healthcare Vertical, MphasiS Limited, Chennai, India.
Ecancermedicalscience. 2011;5:189. doi: 10.3332/ecancer.2011.189. Epub 2011 Feb 22.
One of the most promising avenues for interpreting large datasets of molecular expression profiles involves pathway-based analysis. Pathways are collection of genes and proteins that perform a well-defined biological task. These pathways have been established through decades of molecular biology research and are collected in a variety of public pathway repositories (KEGG and Reactome Pathway database). Understanding the complexity of these pathways is critical for understanding normal biological conditions and disease states and also since the number of known pathways within the cells is significantly smaller than the number of genes that is typically profiled, the transformation of data from a gene-centric view to a pathway-centred one represents a dramatic reduction in the number of dimensions. Such reduction allows a biologist to interpret and understand the data in a manner that is not possible when it is viewed as a collection of individual genes.
解释大型分子表达谱数据集最有前景的途径之一是基于通路的分析。通路是执行明确生物学任务的基因和蛋白质集合。这些通路是经过数十年分子生物学研究建立起来的，并收集在各种公共通路知识库（KEGG和Reactome通路数据库）中。理解这些通路的复杂性对于理解正常生物学状况和疾病状态至关重要，而且由于细胞内已知通路的数量明显少于通常所分析的基因数量，将数据从以基因为中心的视角转换为以通路为中心的视角意味着维度数量的大幅减少。这种减少使生物学家能够以一种在将数据视为单个基因集合时无法实现的方式来解释和理解数据。
