Literature DB >> 23894105

Controlling for confounding variables in MS-omics protocol: why modularity matters.

Rob Smith, Dan Ventura, John T Prince.   

Abstract

As the field of bioinformatics research continues to grow, more and more novel techniques are proposed to meet new challenges and improvements upon solutions to long-standing problems. These include data processing techniques and wet lab protocol techniques. Although the literature is consistently thorough in experimental detail and variable-controlling rigor for wet lab protocol techniques, bioinformatics techniques tend to be less described and less controlled. As the validation or rejection of hypotheses rests on the experiment's ability to isolate and measure a variable of interest, we urge the importance of reducing confounding variables in bioinformatics techniques during mass spectrometry experimentation.
© The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Keywords:  alignment; classification; noise reduction; parameter selection; peak picking

Mesh:

Substances:

Year:  2013        PMID: 23894105     DOI: 10.1093/bib/bbt049

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  5 in total

1.  Virtual Reference Environments: a simple way to make research reproducible.

Authors:  Daniel G Hurley; David M Budden; Edmund J Crampin
Journal:  Brief Bioinform       Date:  2014-11-28       Impact factor: 11.622

2.  Current controlled vocabularies are insufficient to uniquely map molecular entities to mass spectrometry signal.

Authors:  Rob Smith; Ryan M Taylor; John T Prince
Journal:  BMC Bioinformatics       Date:  2015-04-23       Impact factor: 3.169

3.  Fast, axis-agnostic, dynamically summarized storage and retrieval for mass spectrometry data.

Authors:  Kyle Handy; Jebediah Rosen; André Gillan; Rob Smith
Journal:  PLoS One       Date:  2017-11-15       Impact factor: 3.240

Review 4.  Metabolomics Biomarkers for Detection of Colorectal Neoplasms: A Systematic Review.

Authors:  Vanessa Erben; Megha Bhardwaj; Petra Schrotz-King; Hermann Brenner
Journal:  Cancers (Basel)       Date:  2018-07-27       Impact factor: 6.639

5.  A novel procedure for statistical inference and verification of gene regulatory subnetwork.

Authors:  Haijun Gong; Jakob Klinger; Kevin Damazyn; Xiangrui Li; Shiyang Huang
Journal:  BMC Bioinformatics       Date:  2015-04-23       Impact factor: 3.169

  5 in total

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