Evaluating the effectiveness of sequence analysis algorithms using measures of relevant information

摘要

Given vast quantities of molecular sequence data, and numerous different algorithms designed to discover, diagnose or model biologically interesting features in sequences, how is it possible to make objective evaluations of the diagnostic effectiveness of these algorithms and robust assessments of their relative strengths and limitations? An approach to this relatively neglected question is developed here, which is based on information measures of the diagnostic efficiency of different methods. From output lists of a procedure such as a database search, "relevance weights" are assigned that encode, for each sequence listed, the level of associated scientific evidence implicating that sequence as an example of a feature of interest.