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PON-P and PON-P2 predictor performance in CAGI challenges : Lessons learned

Niroula, Abhishek LU and Vihinen, Mauno LU (2017) In Human Mutation 38(9). p.1085-1091
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... (More)

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.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
CAGI, Mutation prediction, Performance assessment measures, PON-P, PON-P2, Variation benchmarks, Variation interpretation
in
Human Mutation
volume
38
issue
9
pages
1085 - 1091
publisher
John Wiley & Sons
external identifiers
  • scopus:85018724100
  • wos:000407861100006
ISSN
1059-7794
DOI
10.1002/humu.23199
language
English
LU publication?
yes
id
7d3cdb1c-6324-4d2a-8674-68d1085b5078
date added to LUP
2017-06-01 13:04:38
date last changed
2018-03-04 05:02:23
@article{7d3cdb1c-6324-4d2a-8674-68d1085b5078,
  abstract     = {<p>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.</p>},
  author       = {Niroula, Abhishek and Vihinen, Mauno},
  issn         = {1059-7794},
  keyword      = {CAGI,Mutation prediction,Performance assessment measures,PON-P,PON-P2,Variation benchmarks,Variation interpretation},
  language     = {eng},
  number       = {9},
  pages        = {1085--1091},
  publisher    = {John Wiley & Sons},
  series       = {Human Mutation},
  title        = {PON-P and PON-P2 predictor performance in CAGI challenges : Lessons learned},
  url          = {http://dx.doi.org/10.1002/humu.23199},
  volume       = {38},
  year         = {2017},
}