Literature DB >> 15340433

Towards sound epistemological foundations of statistical methods for high-dimensional biology.

Tapan Mehta1, Murat Tanik, David B Allison.   

Abstract

A sound epistemological foundation for biological inquiry comes, in part, from application of valid statistical procedures. This tenet is widely appreciated by scientists studying the new realm of high-dimensional biology, or 'omic' research, which involves multiplicity at unprecedented scales. Many papers aimed at the high-dimensional biology community describe the development or application of statistical techniques. The validity of many of these is questionable, and a shared understanding about the epistemological foundations of the statistical methods themselves seems to be lacking. Here we offer a framework in which the epistemological foundation of proposed statistical methods can be evaluated.

Mesh:

Year:  2004        PMID: 15340433     DOI: 10.1038/ng1422

Source DB:  PubMed          Journal:  Nat Genet        ISSN: 1061-4036            Impact factor:   38.330


  46 in total

1.  Empirical evaluation of data transformations and ranking statistics for microarray analysis.

Authors:  Li-Xuan Qin; Kathleen F Kerr
Journal:  Nucleic Acids Res       Date:  2004-10-12       Impact factor: 16.971

2.  Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery.

Authors:  Roberto Romero; Shali Mazaki-Tovi; Edi Vaisbuch; Juan Pedro Kusanovic; Tinnakorn Chaiworapongsa; Ricardo Gomez; Jyh Kae Nien; Bo Hyun Yoon; Moshe Mazor; Jingqin Luo; David Banks; John Ryals; Chris Beecher
Journal:  J Matern Fetal Neonatal Med       Date:  2010-05-26

3.  Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data.

Authors:  Stephen Barnes; H Paul Benton; Krista Casazza; Sara J Cooper; Xiangqin Cui; Xiuxia Du; Jeffrey Engler; Janusz H Kabarowski; Shuzhao Li; Wimal Pathmasiri; Jeevan K Prasain; Matthew B Renfrow; Hemant K Tiwari
Journal:  J Mass Spectrom       Date:  2016-07       Impact factor: 1.982

4.  Analysis of microarray experiments of gene expression profiling.

Authors:  Adi L Tarca; Roberto Romero; Sorin Draghici
Journal:  Am J Obstet Gynecol       Date:  2006-08       Impact factor: 8.661

Review 5.  Statistical issues in clinical trial design.

Authors:  Kenneth R Hess
Journal:  Curr Oncol Rep       Date:  2007-01       Impact factor: 5.075

Review 6.  The use of high-dimensional biology (genomics, transcriptomics, proteomics, and metabolomics) to understand the preterm parturition syndrome.

Authors:  R Romero; J Espinoza; F Gotsch; J P Kusanovic; L A Friel; O Erez; S Mazaki-Tovi; N G Than; S Hassan; G Tromp
Journal:  BJOG       Date:  2006-12       Impact factor: 6.531

7.  Decorrelation of the true and estimated classifier errors in high-dimensional settings.

Authors:  Blaise Hanczar; Jianping Hua; Edward R Dougherty
Journal:  EURASIP J Bioinform Syst Biol       Date:  2007

8.  Validation of computational methods in genomics.

Authors:  Edward R Doughtery; Hua Jianping; Michael L Bittner
Journal:  Curr Genomics       Date:  2007-03       Impact factor: 2.236

9.  The Beta-Binomial Distribution for Estimating the Number of False Rejections in Microarray Gene Expression Studies.

Authors:  Daniel L Hunt; Cheng Cheng; Stanley Pounds
Journal:  Comput Stat Data Anal       Date:  2009-03-15       Impact factor: 1.681

10.  Interdependence of signal processing and analysis of urine 1H NMR spectra for metabolic profiling.

Authors:  Shucha Zhang; Cheng Zheng; Ian R Lanza; K Sreekumaran Nair; Daniel Raftery; Olga Vitek
Journal:  Anal Chem       Date:  2009-08-01       Impact factor: 6.986

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