Associative clustering for exploring dependencies between functional genomics data sets

Kaski S.; Nikkila J.; Sinkkonen J.; Lahti L.; Knuuttila J.E.A.; Roos C.

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Associative clustering for exploring dependencies between functional genomics data sets

机译：关联聚类，用于探索功能基因组学数据集之间的依赖性

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High-throughput genomic measurements, interpreted as cooccurring data samples from multiple sources, open up a fresh problem for machine learning: What is in common in the different data sets, that is, what kind of statistical dependencies are there between the paired samples from the different sets? We introduce a clustering algorithm for exploring the dependencies. Samples within each data set are grouped such that the dependencies between groups of different sets capture as much of pairwise dependencies between the samples as possible. We formalize this problem in a novel probabilistic way, as optimization of a Bayes factor. The method is applied to reveal commonalities and exceptions in gene expression between organisms and to suggest regulatory interactions in the form of dependencies between gene expression profiles and regulator binding patterns.

机译：高通量基因组测量被解释为来自多个来源的同现数据样本，这为机器学习提出了一个新的问题：不同数据集的共同点是，即来自不同来源的成对样本之间存在什么样的统计依赖性。不同的集？我们介绍了一种用于探索依赖关系的聚类算法。将每个数据集中的样本进行分组，以使不同集合的组之间的依存关系捕获尽可能多的样本之间的成对依存关系。我们以一种新颖的概率方式将此问题形式化，作为贝叶斯因子的优化。该方法适用于揭示生物之间基因表达的共性和例外，并以基因表达谱和调节剂结合模式之间的依赖性形式建议调节相互作用。

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《IEEE/ACM transactions on computational biology and bioinformatics》 |2005年第3期|p.203-216|共14页
作者
Kaski S.; Nikkila J.; Sinkkonen J.; Lahti L.; Knuuttila J.E.A.; Roos C.;
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正文语种 eng
中图分类生物数学方法;生物信息论;
关键词
Bayes methods; biology computing; genetics; learning (artificial intelligence); molecular biophysics; optimisation; statistical analysis; Bayes factor; associative clustering; clustering algorithm; functional genomics data sets; gene expression; machine learning; opt;

机译：贝叶斯方法;生物学计算;遗传学;学习（人工智能）;分子生物物理学;优化;统计分析;贝叶斯因子;关联聚类;聚类算法;功能基因组学数据集;基因表达;机器学习;选择;

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1. A novel regulatory event-based gene set analysis method for exploring global functional changes in heterogeneous genomic data sets [J] . Chien-Yi Tung, Chih-Hung Jen, Ming-Ta Hsu, BMC Genomics . 2009,第1期

机译：一种新颖的基于监管事件的基因集分析方法，用于探索异构基因组数据集中的全局功能变化
2. SnpHub: an easy-to-set-up web server framework for exploring large-scale genomic variation data in the post-genomic era with applications in wheat [J] . Wang Wenxi, Wang Zihao, Li Xintong, GigaScience . 2020,第6期

机译：Snhub：一种易于设置的Web服务器框架，用于探索基因组中的大规模基因组变异数据，在小麦中的应用
3. Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set [J] . Jonathan D Wren, David Johnson, Le Gruenwald BMC Bioinformatics . 2005,第SUPPLEMENTa2期

机译：通过基于序列的矩阵格式和关联规则集自动进行基因组数据挖掘
4. Exploring Protein Functional Relationships Using Genomic Information and Data Mining Techniques [C] . Jack Y.Yang, Mary Qu Yang, Okan K. Ersoy Joint International Conference on Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003 Jun 26-29, 2003 Istanbul, Turkey . 2003

机译：使用基因组信息和数据挖掘技术探索蛋白质功能关系
5. Clustering and Registration of Functional Data with Applications in Time Course Genomics Data. [D] . Zhang, Yafeng. 2014

机译：功能数据的聚类和注册以及时程基因组数据中的应用程序。
6. A novel regulatory event-based gene set analysis method for exploring global functional changes in heterogeneous genomic data sets [O] . Chien-Yi Tung, Chih-Hung Jen, Ming-Ta Hsu, 2009

机译：一种新颖的基于监管事件的基因集分析方法用于探索异构基因组数据集中的全局功能变化
7. Associative Clustering for Exploring Dependencies between Functional Genomics Data Sets [O] . Samuel Kaski, Senior Member, Janne Nikkilä, 2005

机译：用于探索功能基因组学数据集之间依赖关系的关联聚类

Associative clustering for exploring dependencies between functional genomics data sets

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