Probabilistic retrieval and visualization of biologically relevant microarray experiments
Probabilistic retrieval and visualization of biologically relevant microarray experiments
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DOI：
10.1093/bioinformatics/btp215
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发表时间：
2009-06-15
期刊：
影响因子：
5.8
通讯作者：
Kaski, Samuel
中科院分区：
文献类型：
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作者：
Caldas, Jose;Gehlenborg, Nils;Kaski, Samuel
动机：随着ArrayExpress和其他全基因组实验库的成熟规模，考虑到特定的研究，搜索相关实验变得更有意义。我们引入了允许基于测量数据而不是更常用的注释数据进行搜索的方法。其目标是检索在其中激活了相同生物过程的实验。这可能是由于针对相同生物问题的实验，也可能是由于未知的关系。结果：我们使用现有的和新的概率机器学习技术相结合的方法来提取关于每个实验中不同激活的生物过程的信息，检索早期激活相同过程的实验，并可视化和解释检索结果。对ArrayExpress子集的案例研究表明，在有足够数量的数据的情况下，我们的方法确实找到了与特定生物学问题相关的实验。结果可以用可视化技术从生物过程的角度来解释。
Motivation: As ArrayExpress and other repositories of genome-wide experiments are reaching a mature size, it is becoming more meaningful to search for related experiments, given a particular study. We introduce methods that allow for the search to be based upon measurement data, instead of the more customary annotation data. The goal is to retrieve experiments in which the same biological processes are activated. This can be due either to experiments targeting the same biological question, or to as yet unknown relationships.Results: We use a combination of existing and new probabilistic machine learning techniques to extract information about the biological processes differentially activated in each experiment, to retrieve earlier experiments where the same processes are activated and to visualize and interpret the retrieval results. Case studies on a subset of ArrayExpress show that, with a sufficient amount of data, our method indeed finds experiments relevant to particular biological questions. Results can be interpreted in terms of biological processes using the visualization techniques.