Statistical and machine learning approaches play an increasingly important role in biomedical research. In the absence of fundamental (first principle-based) models, or because of the computational complexity of such models, statistical and machine learning approaches are being used to identify interesting structures in the data (e.g. patterns in gene expression profiles), correlate these patterns and other «input» attributes with (e.g. medically) relevant outcomes, and to develop predictors that can generalize from known data and make predictions for new data instances. Examples of important applications include structural bioinformatics, in which one of the goals is to predict elements of protein structure from amino acid sequence, or microarray gene expression profiling, in which the goal is to discover interesting patterns in gene expression data and correlate them with clinically relevant phenotypes. This volume includes papers submitted to the BIT 2005 workshop on the Applications of Machine and Statistical Learning Methods in Bioinformatics that took place in September 2005 in Torun, Poland.
