Assessing the Performance of Macromolecular Sequence Classifiers

Machine learning approaches offer some of the most cost-effective approaches to building predictive models (e.g., classifiers) in a broad range of applications in computational biology. Comparing the effectiveness of different algorithms requires reliable procedures for accurately assessing the performance (e.g., accuracy, sensitivity, and specificity) of the resulting predictive classifiers. The difficulty of this task is compounded by the use of different data selection and evaluation procedures and in some cases, even different definitions for the same performance measures. We explore the problem of assessing the performance of predictive classifiers trained on macromolecular sequence data, with an emphasis on cross-validation and data selection methods. Specifically, we compare sequence-based and window-based cross-validation procedures on three sequence-based prediction tasks: identification of glycosylation sites, RNA-Protein interface residues, and Protein-Protein interface resi...

• Type: Conference
• Year: 2007
• Where: BIBE
• Authors: Cornelia Caragea, Jivko Sinapov, Vasant Honavar, Drena Dobbs
• Keywords: BIBE 2007, Bioinformatics, Data Selection, Data Selection Methods, Predictive Classifiers