Larrañaga Pedro, Calvo Borja, Santana Roberto, Bielza Concha, Galdiano Josu, Inza Iñaki, Lozano José A, Armañanzas Rubén, Santafé Guzmán, Pérez Aritz, Robles Victor
Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain.
Brief Bioinform. 2006 Mar;7(1):86-112. doi: 10.1093/bib/bbk007.
This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.
本文综述了生物信息学中的机器学习方法。它介绍了用于知识发现的建模方法，如监督分类、聚类和概率图形模型，以及用于优化的确定性和随机启发式方法。还展示了其在基因组学、蛋白质组学、系统生物学、进化和文本挖掘中的应用。
