Literature DB >> 23037799

Scientific research in the age of omics: the good, the bad, and the sloppy.

Daniela M Witten1, Robert Tibshirani.   

Abstract

It has been claimed that most research findings are false, and it is known that large-scale studies involving omics data are especially prone to errors in design, execution, and analysis. The situation is alarming because taxpayer dollars fund a substantial amount of biomedical research, and because the publication of a research article that is later determined to be flawed can erode the credibility of an entire field, resulting in a severe and negative impact for years to come. Here, we urge the development of an online, open-access, postpublication, peer review system that will increase the accountability of scientists for the quality of their research and the ability of readers to distinguish good from sloppy science.

Mesh:

Year:  2012        PMID: 23037799      PMCID: PMC3555320          DOI: 10.1136/amiajnl-2012-000972

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  15 in total

1.  Reproducible research in computational science.

Authors:  Roger D Peng
Journal:  Science       Date:  2011-12-02       Impact factor: 47.728

2.  Analyzing gene expression data in terms of gene sets: methodological issues.

Authors:  Jelle J Goeman; Peter Bühlmann
Journal:  Bioinformatics       Date:  2007-02-15       Impact factor: 6.937

3.  Comment on "The consensus coding sequences of human breast and colorectal cancers".

Authors:  Gad Getz; Holger Höfling; Jill P Mesirov; Todd R Golub; Matthew Meyerson; Robert Tibshirani; Eric S Lander
Journal:  Science       Date:  2007-09-14       Impact factor: 47.728

4.  Improved detection of overrepresentation of Gene-Ontology annotations with parent child analysis.

Authors:  Steffen Grossmann; Sebastian Bauer; Peter N Robinson; Martin Vingron
Journal:  Bioinformatics       Date:  2007-09-11       Impact factor: 6.937

5.  Devil in the details.

Authors: 
Journal:  Nature       Date:  2011-02-17       Impact factor: 49.962

6.  Peer review: Trial by Twitter.

Authors:  Apoorva Mandavilli
Journal:  Nature       Date:  2011-01-20       Impact factor: 49.962

7.  Making data maximally available.

Authors:  Brooks Hanson; Andrew Sugden; Bruce Alberts
Journal:  Science       Date:  2011-02-11       Impact factor: 47.728

8.  GO PaD: the Gene Ontology Partition Database.

Authors:  Gil Alterovitz; Michael Xiang; Mamta Mohan; Marco F Ramoni
Journal:  Nucleic Acids Res       Date:  2006-11-10       Impact factor: 16.971

9.  Why most published research findings are false.

Authors:  John P A Ioannidis
Journal:  PLoS Med       Date:  2005-08-30       Impact factor: 11.613

10.  How to decide which are the most pertinent overly-represented features during gene set enrichment analysis.

Authors:  Roland Barriot; David J Sherman; Isabelle Dutour
Journal:  BMC Bioinformatics       Date:  2007-09-11       Impact factor: 3.169

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  7 in total

1.  Increasing value and reducing waste in research design, conduct, and analysis.

Authors:  John P A Ioannidis; Sander Greenland; Mark A Hlatky; Muin J Khoury; Malcolm R Macleod; David Moher; Kenneth F Schulz; Robert Tibshirani
Journal:  Lancet       Date:  2014-01-08       Impact factor: 79.321

2.  How to make more published research true.

Authors:  John P A Ioannidis
Journal:  PLoS Med       Date:  2014-10-21       Impact factor: 11.069

3.  Distinct Antigen Delivery Systems Induce Dendritic Cells' Divergent Transcriptional Response: New Insights from a Comparative and Reproducible Computational Analysis.

Authors:  Valerio Costa; Dario Righelli; Francesco Russo; Piergiuseppe De Berardinis; Claudia Angelini; Luciana D'Apice
Journal:  Int J Mol Sci       Date:  2017-02-25       Impact factor: 5.923

4.  Making simulation results reproducible-Survey, guidelines, and examples based on Gradle and Docker.

Authors:  Wilfried Elmenreich; Philipp Moll; Sebastian Theuermann; Mathias Lux
Journal:  PeerJ Comput Sci       Date:  2019-12-09

Review 5.  Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer.

Authors:  Levi Waldron; Benjamin Haibe-Kains; Aedín C Culhane; Markus Riester; Jie Ding; Xin Victoria Wang; Mahnaz Ahmadifar; Svitlana Tyekucheva; Christoph Bernau; Thomas Risch; Benjamin Frederick Ganzfried; Curtis Huttenhower; Michael Birrer; Giovanni Parmigiani
Journal:  J Natl Cancer Inst       Date:  2014-04-03       Impact factor: 11.816

6.  Discovery of A-type procyanidin dimers in yellow raspberries by untargeted metabolomics and correlation based data analysis.

Authors:  Elisabete Carvalho; Pietro Franceschi; Antje Feller; Lorena Herrera; Luisa Palmieri; Panagiotis Arapitsas; Samantha Riccadonna; Stefan Martens
Journal:  Metabolomics       Date:  2016-08-08       Impact factor: 4.290

Review 7.  Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data.

Authors:  Antonio Federico; Angela Serra; My Kieu Ha; Pekka Kohonen; Jang-Sik Choi; Irene Liampa; Penny Nymark; Natasha Sanabria; Luca Cattelani; Michele Fratello; Pia Anneli Sofia Kinaret; Karolina Jagiello; Tomasz Puzyn; Georgia Melagraki; Mary Gulumian; Antreas Afantitis; Haralambos Sarimveis; Tae-Hyun Yoon; Roland Grafström; Dario Greco
Journal:  Nanomaterials (Basel)       Date:  2020-05-08       Impact factor: 5.076

  7 in total

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