Literature DB >> 20407946

Statistical contributions to proteomic research.

Jeffrey S Morris1, Keith A Baggerly, Howard B Gutstein, Kevin R Coombes.   

Abstract

Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the key statistical principles that should guide the experimental design and analysis of such studies.

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Year:  2010        PMID: 20407946      PMCID: PMC3889133          DOI: 10.1007/978-1-60761-711-2_9

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  25 in total

1.  Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values.

Authors:  Stan Pounds; Stephan W Morris
Journal:  Bioinformatics       Date:  2003-07-01       Impact factor: 6.937

2.  High-resolution serum proteomic features for ovarian cancer detection.

Authors:  T P Conrads; V A Fusaro; S Ross; D Johann; V Rajapakse; B A Hitt; S M Steinberg; E C Kohn; D A Fishman; G Whitely; J C Barrett; L A Liotta; E F Petricoin; T D Veenstra
Journal:  Endocr Relat Cancer       Date:  2004-06       Impact factor: 5.678

3.  Analysis of serum proteomic patterns for early cancer diagnosis: drawing attention to potential problems.

Authors:  Eleftherios P Diamandis
Journal:  J Natl Cancer Inst       Date:  2004-03-03       Impact factor: 13.506

Review 4.  Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: opportunities and potential limitations.

Authors:  Eleftherios P Diamandis
Journal:  Mol Cell Proteomics       Date:  2004-02-28       Impact factor: 5.911

Review 5.  The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales.

Authors:  Jianhua Hu; Kevin R Coombes; Jeffrey S Morris; Keith A Baggerly
Journal:  Brief Funct Genomic Proteomic       Date:  2005-02

6.  Pinnacle: a fast, automatic and accurate method for detecting and quantifying protein spots in 2-dimensional gel electrophoresis data.

Authors:  Jeffrey S Morris; Brittan N Clark; Howard B Gutstein
Journal:  Bioinformatics       Date:  2008-01-14       Impact factor: 6.937

7.  Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform.

Authors:  Kevin R Coombes; Spiridon Tsavachidis; Jeffrey S Morris; Keith A Baggerly; Mien-Chie Hung; Henry M Kuerer
Journal:  Proteomics       Date:  2005-11       Impact factor: 3.984

8.  Use of proteomic patterns in serum to identify ovarian cancer.

Authors:  Emanuel F Petricoin; Ali M Ardekani; Ben A Hitt; Peter J Levine; Vincent A Fusaro; Seth M Steinberg; Gordon B Mills; Charles Simone; David A Fishman; Elise C Kohn; Lance A Liotta
Journal:  Lancet       Date:  2002-02-16       Impact factor: 79.321

9.  Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments.

Authors:  Keith A Baggerly; Jeffrey S Morris; Kevin R Coombes
Journal:  Bioinformatics       Date:  2004-01-29       Impact factor: 6.937

10.  An empirical study of univariate and genetic algorithm-based feature selection in binary classification with microarray data.

Authors:  Michael Lecocke; Kenneth Hess
Journal:  Cancer Inform       Date:  2007-02-23
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  3 in total

1.  Statistical Methods for Proteomic Biomarker Discovery based on Feature Extraction or Functional Modeling Approaches.

Authors:  Jeffrey S Morris
Journal:  Stat Interface       Date:  2012-01-01       Impact factor: 0.582

2.  Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups.

Authors:  Ricardo J Pais
Journal:  BioTech (Basel)       Date:  2022-08-17

3.  Importance of Block Randomization When Designing Proteomics Experiments.

Authors:  Bram Burger; Marc Vaudel; Harald Barsnes
Journal:  J Proteome Res       Date:  2020-10-05       Impact factor: 4.466

  3 in total

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