Literature DB >> 24915749

Majority vote and other problems when using computational tools.

Mauno Vihinen1.   

Abstract

Computational tools are essential for most of our research. To use these tools, one needs to know how they work. Problems in application of computational methods to variation analysis can appear at several stages and affect, for example, the interpretation of results. Such cases are discussed along with suggestions how to avoid them. The applications include incomplete reporting of methods, especially about the use of prediction tools; method selection on unscientific grounds and without consulting independent method performance assessments; extending application area of methods outside their intended purpose; use of the same data several times for obtaining majority vote; and filtering of datasets so that variants of interest are excluded. All these issues can be avoided by discontinuing the use software tools as black boxes.
© 2014 WILEY PERIODICALS, INC.

Keywords:  method choice; prediction methods; prediction performance; software tool; variation

Mesh:

Year:  2014        PMID: 24915749     DOI: 10.1002/humu.22600

Source DB:  PubMed          Journal:  Hum Mutat        ISSN: 1059-7794            Impact factor:   4.878


  10 in total

1.  PaPI: pseudo amino acid composition to score human protein-coding variants.

Authors:  Ivan Limongelli; Simone Marini; Riccardo Bellazzi
Journal:  BMC Bioinformatics       Date:  2015-04-19       Impact factor: 3.169

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

Authors:  Abhishek Niroula; Mauno Vihinen
Journal:  Hum Mutat       Date:  2017-05-02       Impact factor: 4.878

3.  HUMA: A platform for the analysis of genetic variation in humans.

Authors:  David K Brown; Özlem Tastan Bishop
Journal:  Hum Mutat       Date:  2017-10-17       Impact factor: 4.878

4.  CHARGE and Kabuki Syndromes: Gene-Specific DNA Methylation Signatures Identify Epigenetic Mechanisms Linking These Clinically Overlapping Conditions.

Authors:  Darci T Butcher; Cheryl Cytrynbaum; Andrei L Turinsky; Michelle T Siu; Michal Inbar-Feigenberg; Roberto Mendoza-Londono; David Chitayat; Susan Walker; Jerry Machado; Oana Caluseriu; Lucie Dupuis; Daria Grafodatskaya; William Reardon; Brigitte Gilbert-Dussardier; Alain Verloes; Frederic Bilan; Jeff M Milunsky; Raveen Basran; Blake Papsin; Tracy L Stockley; Stephen W Scherer; Sanaa Choufani; Michael Brudno; Rosanna Weksberg
Journal:  Am J Hum Genet       Date:  2017-05-04       Impact factor: 11.025

5.  Development of pathogenicity predictors specific for variants that do not comply with clinical guidelines for the use of computational evidence.

Authors:  Elena Álvarez de la Campa; Natàlia Padilla; Xavier de la Cruz
Journal:  BMC Genomics       Date:  2017-08-11       Impact factor: 3.969

6.  Accurate Classification of NF1 Gene Variants in 84 Italian Patients with Neurofibromatosis Type 1.

Authors:  Alessandro Stella; Patrizia Lastella; Daria Carmela Loconte; Nenad Bukvic; Dora Varvara; Margherita Patruno; Rosanna Bagnulo; Rosaura Lovaglio; Nicola Bartolomeo; Gabriella Serio; Nicoletta Resta
Journal:  Genes (Basel)       Date:  2018-04-17       Impact factor: 4.096

7.  Representativeness of variation benchmark datasets.

Authors:  Gerard C P Schaafsma; Mauno Vihinen
Journal:  BMC Bioinformatics       Date:  2018-11-29       Impact factor: 3.169

8.  New insights into the pathogenicity of non-synonymous variants through multi-level analysis.

Authors:  Hong Sun; Guangjun Yu
Journal:  Sci Rep       Date:  2019-02-07       Impact factor: 4.379

9.  Problems in variation interpretation guidelines and in their implementation in computational tools.

Authors:  Mauno Vihinen
Journal:  Mol Genet Genomic Med       Date:  2020-03-11       Impact factor: 2.183

Review 10.  Challenges in Clinicogenetic Correlations: One Phenotype - Many Genes.

Authors:  Rahul Gannamani; Sterre van der Veen; Martje van Egmond; Tom J de Koning; Marina A J Tijssen
Journal:  Mov Disord Clin Pract       Date:  2021-03-02
  10 in total

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