Guidelines for reporting and using prediction tools for genetic variation analysis
Vihinen, Mauno LU
(2013)
In Human Mutation
34(2).
p.275-282
- Abstract
- Computational prediction methods are widely used for analysis of human genome sequence variants and their effects on gene/protein function, splice site aberration, pathogenicity, and disease risk. New methods are frequently developed. We believe that guidelines are essential for those writing articles about new prediction methods, as well as for those applying these tools in their research, so that the necessary details are reported. This will enable readers to gain the full picture of technical information, performance, and interpretation of results, and to facilitate comparisons of related methods. Here we provide instructions on how to describe new methods, report datasets, and assess the performance of predictive tools. We also discuss... (More)
- Computational prediction methods are widely used for analysis of human genome sequence variants and their effects on gene/protein function, splice site aberration, pathogenicity, and disease risk. New methods are frequently developed. We believe that guidelines are essential for those writing articles about new prediction methods, as well as for those applying these tools in their research, so that the necessary details are reported. This will enable readers to gain the full picture of technical information, performance, and interpretation of results, and to facilitate comparisons of related methods. Here we provide instructions on how to describe new methods, report datasets, and assess the performance of predictive tools. We also discuss what details of predictor implementation are essential for authors to understand. Similarly, these guidelines for the use of predictors provide instructions on what needs to be delineated in the text, as well as how researchers can avoid unwarranted conclusions. They are applicable to most prediction methods currently utilized. By applying these guidelines, authors will help reviewers, editors, and readers to more fully comprehend prediction methods and their use. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3218711
- author
- Vihinen, Mauno
LU
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Human Mutation
- volume
- 34
- issue
- 2
- pages
- 275 - 282
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- wos:000314477700001
- pmid:23169447
- scopus:84873087051
- pmid:23169447
- ISSN
- 1059-7794
- DOI
- 10.1002/humu.22253
- language
- English
- LU publication?
- yes
- id
- cc94e444-4fed-4c66-b1ac-29db2bf2f989 (old id 3218711)
- alternative location
- http://www.ncbi.nlm.nih.gov/pubmed/23169447?dopt=Abstract
- date added to LUP
- 2016-04-04 08:39:23
- date last changed
- 2022-03-15 08:35:02
@article{cc94e444-4fed-4c66-b1ac-29db2bf2f989,
abstract = {{Computational prediction methods are widely used for analysis of human genome sequence variants and their effects on gene/protein function, splice site aberration, pathogenicity, and disease risk. New methods are frequently developed. We believe that guidelines are essential for those writing articles about new prediction methods, as well as for those applying these tools in their research, so that the necessary details are reported. This will enable readers to gain the full picture of technical information, performance, and interpretation of results, and to facilitate comparisons of related methods. Here we provide instructions on how to describe new methods, report datasets, and assess the performance of predictive tools. We also discuss what details of predictor implementation are essential for authors to understand. Similarly, these guidelines for the use of predictors provide instructions on what needs to be delineated in the text, as well as how researchers can avoid unwarranted conclusions. They are applicable to most prediction methods currently utilized. By applying these guidelines, authors will help reviewers, editors, and readers to more fully comprehend prediction methods and their use.}},
author = {{Vihinen, Mauno}},
issn = {{1059-7794}},
language = {{eng}},
number = {{2}},
pages = {{275--282}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Human Mutation}},
title = {{Guidelines for reporting and using prediction tools for genetic variation analysis}},
url = {{http://dx.doi.org/10.1002/humu.22253}},
doi = {{10.1002/humu.22253}},
volume = {{34}},
year = {{2013}},
}