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.
(Less)
- author
- Niroula, Abhishek
LU
and Vihinen, Mauno
LU
- organization
- publishing date
- 2017-04
- 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 Inc.
- external identifiers
-
- pmid:28224672
- wos:000407861100006
- scopus:85018724100
- 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
- 2025-01-07 14:33:43
@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}},
keywords = {{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 Inc.}},
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}},
doi = {{10.1002/humu.23199}},
volume = {{38}},
year = {{2017}},
}