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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)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Human Mutation
volume
34
issue
2
pages
275 - 282
publisher
John Wiley and 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
2020-10-07 04:33:05
@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 and 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},
}