Resumen: Data mining and machine learning are two central areas of bioinformatics. During the last decades, we have in my group developed a large panel of machine learning methods suitable for data mining and pattern recognition in biological data. Most of the methods are hybrids of standard machine learning methods including linear regression, Gibbs sampling, and artificial neural networks. Although very simple, these methods have proven highly accurate when it comes to the extraction of receptor binding motifs from complex data. In my presentation, I will describe the background of some of these methods, illustrate their functionality on biological data.

Informes: jechave@unsam.edu.ar