School of Computer Science, Nottingham University, Jubilee Campus, Wollaton Road, Nottingham, UK.
BMC Bioinformatics. 2009 Oct 28;10:358. doi: 10.1186/1471-2105-10-358.
Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks.
We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining.
ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.
DNA 微阵列数据分析提供了一种有价值的诊断工具，可用于研究疾病的遗传成分。为了充分利用大量可用的数据集和分析方法，理想情况下是将不同的算法和来自不同研究的数据结合起来。为此，应用集成学习、共识聚类和跨研究标准化方法，在几乎完全自动化的过程中，将不同的分析模块连接在一起，并在单个接口下链接不同的分析模块，这将简化许多微阵列分析任务。
我们提出了 ArrayMining.net，这是一个用于微阵列分析的网络应用程序，它提供了轻松访问广泛的特征选择、聚类、预测、基因集分析和跨研究标准化方法的途径。与其他与微阵列相关的网络工具不同，多个算法和数据集可以通过集成特征选择、集成预测、共识聚类和跨平台数据集成来组合用于分析任务。通过以模块化的方式链接不同的分析工具，新的探索途径变得可用，例如使用来自基因集分析的特征和来自多个研究的数据进行集成样本分类。通过自动参数选择机制以及与功能注释和文献挖掘的网络工具和数据库的链接，进一步简化了分析。
ArrayMining.net 是一个免费的微阵列分析网络应用程序，它结合了基于集成和共识方法的广泛算法选择，使用自动参数选择和与注释数据库的集成。
