Nikolajewa Swetlana, Pudimat Rainer, Hiller Michael, Platzer Matthias, Backofen Rolf
Department of Bioinformatics, Friedrich-Schiller-University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W688-93. doi: 10.1093/nar/gkm292. Epub 2007 May 30.
BioBayesNet is a new web application that allows the easy modeling and classification of biological data using Bayesian networks. To learn Bayesian networks the user can either upload a set of annotated FASTA sequences or a set of pre-computed feature vectors. In case of FASTA sequences, the server is able to generate a wide range of sequence and structural features from the sequences. These features are used to learn Bayesian networks. An automatic feature selection procedure assists in selecting discriminative features, providing an (locally) optimal set of features. The output includes several quality measures of the overall network and individual features as well as a graphical representation of the network structure, which allows to explore dependencies between features. Finally, the learned Bayesian network or another uploaded network can be used to classify new data. BioBayesNet facilitates the use of Bayesian networks in biological sequences analysis and is flexible to support modeling and classification applications in various scientific fields. The BioBayesNet server is available at http://biwww3.informatik.uni-freiburg.de:8080/BioBayesNet/.
BioBayesNet是一个新的网络应用程序,它允许使用贝叶斯网络轻松地对生物数据进行建模和分类。为了学习贝叶斯网络,用户可以上传一组带注释的FASTA序列或一组预先计算的特征向量。如果上传的是FASTA序列,服务器能够从这些序列中生成广泛的序列和结构特征。这些特征用于学习贝叶斯网络。一个自动特征选择程序有助于选择有区分力的特征,提供一组(局部)最优特征。输出包括整个网络和各个特征的几个质量度量,以及网络结构的图形表示,这允许探索特征之间的依赖性。最后,学习到的贝叶斯网络或另一个上传的网络可用于对新数据进行分类。BioBayesNet便于在生物序列分析中使用贝叶斯网络,并且灵活地支持各种科学领域的建模和分类应用。BioBayesNet服务器可在http://biwww3.informatik.uni-freiburg.de:8080/BioBayesNet/获取。