革新的疾病相關路徑鑑定法：以心肌梗塞跨平台資料為例

吳東懋

doi:10.6831/TMU.2012.00223

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革新的疾病相關路徑鑑定法：以心肌梗塞跨平台資料為例

A Revolutionary Method for Disease Pathways Identification Using Myocardial Infarction Cross-Platform Data

吳東懋 , Masters　　Advisor：李元綺　　

繁體中文 DOI： 10.6831/TMU.2012.00223

基因調控網路；路徑分析； Gene Network ； Pathway Analysis

Abstract │ Reference (30) │ Altmetrics

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Since the day we were born, we have been afflicted with all kinds of illness and diseases. To reduce the pain and fear, all kinds of approaches to attack diseases have been developed. From the aspect of genetics studies, the researchers used to work on a couple of genes that might relate to diseases in all their scientific life. However, along with the advance of biotechnology, we are able to trace and monitor the alteration of gene expression in a whole-genome scale. Furthermore, the chances are the approaches have been moved from individual gene studies to gene-gene interactions and even the gene network, as it is believed that genes are collaboratively functioning in groups instead of working by themselves alone. Therefore, pathway analysis has been getting more attention recently. However, the pathway analysis used update exists many unsolved issues, most importantly, it lacks the integrity of whole-genome regulated networks that actually reflect the gene reaction pathways. We develop a method to identify “Disease-associated Pathways”. Here in this study, we will (1) develop a regulatory gene network by combining each gene-gene reaction pair, (2) identify the shortest pathways from a whole genome network, (3) establish pathway analysis algorithms to calculate the full pathway expression value, and (4) distinguish the disease and non-disease pathways by incorporating microarray data. The purpose of this pipe-line approach is to quest for disease-associated pathways and by unveiling the pathways, it will also provide a better choice for drug target in pharmaceutical industry in the near future.

Reference ( 30 ) 〈TOP〉

1.(2006) The Gene Ontology (GO) project in 2006. Nucleic Acids Res 34: D322-326
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2.(2008) The Gene Ontology project in 2008. Nucleic Acids Res 36: D440-444
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3.Alvo M, Liu Z, Williams A, Yauk C (2010) Testing for mean and correlation changes in microarray experiments: an application for pathway analysis. BMC Bioinformatics 11: 60
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4.Chen L, Xuan J, Gu J, Wang Y, Zhang Z, Wang TL, Shih leM (2012) Integrative network analysis to identify aberrant pathway networks in ovarian cancer. Pac Symp Biocomput 2012:31-42
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5.Fang Z, Tian W, Ji H, (2011) A network-based gene-weighting approach for pathway analysis. Cell Res. 2011 Sep 6. doi:10.1038/cr.2011.149.
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