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口腔基因组学和蛋白质组学中的生物信息学与数据挖掘研究:新趋势与挑战。

Bioinformatics and data mining studies in oral genomics and proteomics: new trends and challenges.

作者信息

Giacomelli Luca, Covani Ugo

机构信息

Tirrenian Stomatologic Institute, Via Aurelia 335, Lido di Camaiore (Lucca), Italy.

出版信息

Open Dent J. 2010 Jul 16;4:67-71. doi: 10.2174/1874210601004020067.

Abstract

Genomics and proteomics have promised to change the practice of dentistry and oral pathology, allowing the identification and the characterization of risk factors and therapeutic targets at a molecular level. However, mass-scale molecular genomics and proteomics suffer from some pitfalls: gene/protein expression are significant only if inserted in a detailed network of molecular pathways and gene/gene, gene/protein and protein/protein interactions. The proper analysis of these complex pictures requires the contribution of theoretical disciplines, like bioinformatics and data mining. In particular, data-mining of existing information could become a strong starting point to formulate new targeted hypotheses and to plan ad hoc experimentation.In this review, advantages and disadvantages of the above-mentioned disciplines and their potential in oral pathology are discussed. The leader gene approach is a new data mining algorithm, recently applied to some oral diseases and their correlation with systemic conditions. The preliminary results of the application of the leader gene approach to the correlation between periodontitis and heart ischemia at a molecular level are presented for the first time.

摘要

基因组学和蛋白质组学有望改变牙科和口腔病理学的实践,能够在分子水平上识别和表征风险因素及治疗靶点。然而,大规模分子基因组学和蛋白质组学存在一些缺陷:基因/蛋白质表达只有嵌入详细的分子途径网络以及基因/基因、基因/蛋白质和蛋白质/蛋白质相互作用中才具有重要意义。对这些复杂情况进行恰当分析需要理论学科的贡献,如生物信息学和数据挖掘。特别是,对现有信息进行数据挖掘可能成为提出新的靶向假设和规划专门实验的有力起点。在本综述中,将讨论上述学科的优缺点及其在口腔病理学中的潜力。主导基因方法是一种新的数据挖掘算法,最近应用于一些口腔疾病及其与全身状况的相关性研究。首次展示了主导基因方法在分子水平上应用于牙周炎与心脏缺血相关性研究的初步结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4997/2945006/0ae04d260223/TODENTJ-4-67_F1.jpg

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