Objective: Identify the set of features that best explained the variation in the performance measure of TREC 2006 Genomics information extraction task, Mean Average Passage Precision (MAPP).
Methods: A multivariate regression model was built using a backward-elimination approach as a function of certain generalized features that were common to all the algorithms used by TREC 2006 Genomics track participants.
Results: Our regression analysis found that the following four factors were collectively associated with variation in MAPP: (1) Normalization of keywords in the query (2) Use of Entrez gene thesaurus for synonymous terms look-up (3) Unit of text retrieved using respective IR algorithms and (4) The way a passage was defined.
Conclusion: These reasonably likely hypotheses, generated by an exploratory data analysis, are informative in understanding results of the TREC 2006 Genomics passage extraction task. This approach has general value for analyzing the results of similar common challenge tasks.