Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.
Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.
复制标题
使用机器学习技术探索全基因组表达谱。
DOI：
10.1007/978-1-4939-6685-1_20
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Papapanou,PanosN
中科院分区：
文献类型：
--
作者：
Kebschull,Moritz;Papapanou,PanosN
尽管当代的高通量组学方法产生了高维数据，但由此产生的丰富信息很难使用传统的统计程序进行评估。机器学习方法有助于检测额外的模式，而不仅仅是识别不同组之间不同的特征列表。在这里，我们展示了(1)监督分类算法在类验证中的效用，(2)无监督聚类在类发现中的效用。我们使用从慢性或侵袭性牙周炎患者获得的牙龈组织样本的转录图谱的数据(1)测试这两个诊断实体是否也具有分子水平上的差异，以及(2)寻找基于组织转录的新的、可选的牙周炎分类。利用机器学习技术，我们提供了当前公认的牙周炎分类中诊断不精确的证据，并证明了基于牙周组织转录差异的新的、可选的分类是可行的。概述的程序允许对特征底层类的高维数据集进行无偏见的询问，并且适用于广泛的组学数据。
Although contemporary high-throughput –omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.Here, we demonstrate the utility of (1) supervised classification algorithms in class validation, and (2) unsupervised clustering in class discovery. We use data from our previous work that described the transcriptional profiles of gingival tissue samples obtained from subjects suffering from chronic or aggressive periodontitis (1) to test whether the two diagnostic entities were also characterized by differences on the molecular level, and (2) to search for a novel, alternative classification of periodontitis based on the tissue transcriptomes.Using machine learning technology, we provide evidence for diagnostic imprecision in the currently accepted classification of periodontitis, and demonstrate that a novel, alternative classification based on differences in gingival tissue transcriptomes is feasible. The outlined procedures allow for the unbiased interrogation of high-dimensional datasets for characteristic underlying classes, and are applicable to a broad range of –omics data.