Literature DB >> 19679825

Stability and aggregation of ranked gene lists.

Anne-Laure Boulesteix1, Martin Slawski.   

Abstract

Ranked gene lists are highly instable in the sense that similar measures of differential gene expression may yield very different rankings, and that a small change of the data set usually affects the obtained gene list considerably. Stability issues have long been under-considered in the literature, but they have grown to a hot topic in the last few years, perhaps as a consequence of the increasing skepticism on the reproducibility and clinical applicability of molecular research findings. In this article, we review existing approaches for the assessment of stability of ranked gene lists and the related problem of aggregation, give some practical recommendations, and warn against potential misuse of these methods. This overview is illustrated through an application to a recent leukemia data set using the freely available Bioconductor package GeneSelector.

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Year:  2009        PMID: 19679825     DOI: 10.1093/bib/bbp034

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


  46 in total

1.  Improving biomarker list stability by integration of biological knowledge in the learning process.

Authors:  Tiziana Sanavia; Fabio Aiolli; Giovanni Da San Martino; Andrea Bisognin; Barbara Di Camillo
Journal:  BMC Bioinformatics       Date:  2012-03-28       Impact factor: 3.169

2.  Combined Plasma and Cerebrospinal Fluid Signature for the Prediction of Midterm Progression From Mild Cognitive Impairment to Alzheimer Disease.

Authors:  Benoit Lehallier; Laurent Essioux; Javier Gayan; Roxana Alexandridis; Tania Nikolcheva; Tony Wyss-Coray; Markus Britschgi
Journal:  JAMA Neurol       Date:  2015-12-14       Impact factor: 18.302

3.  Finding genetic overlaps among diseases based on ranked gene lists.

Authors:  Quan Chen; Xianghong J Zhou; Fengzhu Sun
Journal:  J Comput Biol       Date:  2015-02       Impact factor: 1.479

4.  Empirical evaluation of consistency and accuracy of methods to detect differentially expressed genes based on microarray data.

Authors:  Dake Yang; Rudolph S Parrish; Guy N Brock
Journal:  Comput Biol Med       Date:  2013-12-13       Impact factor: 4.589

5.  Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis.

Authors:  Chuang Ma; Mingming Xin; Kenneth A Feldmann; Xiangfeng Wang
Journal:  Plant Cell       Date:  2014-02-11       Impact factor: 11.277

6.  RIG-I-like receptor LGP2 protects tumor cells from ionizing radiation.

Authors:  Ryan C Widau; Akash D Parekh; Mark C Ranck; Daniel W Golden; Kiran A Kumar; Ravi F Sood; Sean P Pitroda; Zhengkai Liao; Xiaona Huang; Thomas E Darga; David Xu; Lei Huang; Jorge Andrade; Bernard Roizman; Ralph R Weichselbaum; Nikolai N Khodarev
Journal:  Proc Natl Acad Sci U S A       Date:  2014-01-13       Impact factor: 11.205

7.  Assessing the validity and reproducibility of genome-scale predictions.

Authors:  Lauren A Sugden; Michael R Tackett; Yiannis A Savva; William A Thompson; Charles E Lawrence
Journal:  Bioinformatics       Date:  2013-09-17       Impact factor: 6.937

8.  Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.

Authors:  W Shi; M Bessarabova; D Dosymbekov; Z Dezso; T Nikolskaya; M Dudoladova; T Serebryiskaya; A Bugrim; A Guryanov; R J Brennan; R Shah; J Dopazo; M Chen; Y Deng; T Shi; G Jurman; C Furlanello; R S Thomas; J C Corton; W Tong; L Shi; Y Nikolsky
Journal:  Pharmacogenomics J       Date:  2010-08       Impact factor: 3.550

9.  Regularized estimation of large-scale gene association networks using graphical Gaussian models.

Authors:  Nicole Krämer; Juliane Schäfer; Anne-Laure Boulesteix
Journal:  BMC Bioinformatics       Date:  2009-11-24       Impact factor: 3.169

10.  Quantitative comparison of microarray experiments with published leukemia related gene expression signatures.

Authors:  Hans-Ulrich Klein; Christian Ruckert; Alexander Kohlmann; Lars Bullinger; Christian Thiede; Torsten Haferlach; Martin Dugas
Journal:  BMC Bioinformatics       Date:  2009-12-15       Impact factor: 3.169

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