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Literature DB >> 19942585

Over-optimism in bioinformatics research.

Anne-Laure Boulesteix.

Abstract

Mesh：

Year: 2009 PMID： 19942585 DOI： 10.1093/bioinformatics/btp648

Source DB: PubMed Journal: Bioinformatics ISSN： 1367-4803 Impact factor: 6.937

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23 in total

1. FAST: FAST Analysis of Sequences Toolbox.

Authors: Travis J Lawrence; Kyle T Kauffman; Katherine C H Amrine; Dana L Carper; Raymond S Lee; Peter J Becich; Claudia J Canales; David H Ardell
Journal: Front Genet Date: 2015-05-19 Impact factor: 4.599

2. Multiple-rule bias in the comparison of classification rules.

Authors: Mohammadmahdi R Yousefi; Jianping Hua; Edward R Dougherty
Journal: Bioinformatics Date: 2011-05-05 Impact factor: 6.937

3. 2010 translational bioinformatics year in review.

Authors: Russ B Altman; Katharine S Miller
Journal: J Am Med Inform Assoc Date: 2011 Jul-Aug Impact factor: 4.497

4. A new disease-specific machine learning approach for the prediction of cancer-causing missense variants.

Authors: Emidio Capriotti; Russ B Altman
Journal: Genomics Date: 2011-07-07 Impact factor: 5.736

5. High-dimensional bolstered error estimation.

Authors: Chao Sima; Ulisses M Braga-Neto; Edward R Dougherty
Journal: Bioinformatics Date: 2011-09-13 Impact factor: 6.937

6. Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction.

Authors: Anne-Laure Boulesteix; Carolin Strobl
Journal: BMC Med Res Methodol Date: 2009-12-21 Impact factor: 4.615

Over-optimism in bioinformatics research.

1. FAST: FAST Analysis of Sequences Toolbox.

2. Multiple-rule bias in the comparison of classification rules.

3. 2010 translational bioinformatics year in review.

4. A new disease-specific machine learning approach for the prediction of cancer-causing missense variants.

5. High-dimensional bolstered error estimation.

6. Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction.

7. Performance reproducibility index for classification.

8. Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities.

9. Testing the additional predictive value of high-dimensional molecular data.

10. FACT: functional annotation transfer between proteins with similar feature architectures.