title: PON-P and PON-P2 predictor performance in CAGI challenges: Lessons learned.
abstract: Computational tools are widely used for ranking and prioritizing variants for characterizing their disease relevance. Since numerous tools have been developed, they have to be properly assessed before being applied. Critical Assessment of Genome Interpretation (CAGI) experiments have significantly contributed toward the assessment of prediction methods for various tasks. Within and outside the CAGI, we have addressed several questions that facilitate development and assessment of variation interpretation tools. These areas include collection and distribution of benchmark datasets, their use for systematic large-scale method assessment, and the development of guidelines for reporting methods and their performance. For us, CAGI has provided a chance to experiment with new ideas, test the application areas of our methods, and network with other prediction method developers. In this article, we discuss our experiences and lessons learned from the various CAGI challenges. We describe our approaches, their performance, and impact of CAGI on our research. Finally, we discuss some of the possibilities that CAGI experiments have opened up and make some suggestions for future experiments.
authors.affiliation: Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden.
authors.forename: Abhishek
authors.initials: A
authors.lastname: Niroula
authors.affiliation: Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden.
authors.forename: Mauno
authors.initials: M
authors.lastname: Vihinen
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020022:Genetic Predisposition to Disease; D006801:Humans; D009154:Mutation
keywords: CAGI; PON-P; PON-P2; mutation prediction; performance assessment measures; variation benchmarks; variation interpretation