ArticleJournal of biomedicine & biotechnology2010
Stability of ranked gene lists in large microarray analysis studies.
Article in Journal of biomedicine & biotechnology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- A comprehensive empirical comparison of hubness reduction in high-dimensional spaces.Knowledge and information systems · 2019Article
- Fast, Accurate, and Stable Feature Selection Using Neural Networks.Neuroinformatics · 2018Article
- Deep Learning Methods for Predicting Disease Status Using Genomic Data.Journal of biometrics & biostatistics · 2018Article
- A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data.Computational and mathematical methods in medicine · 2017Article
- Comprehensive decision tree models in bioinformatics.PloS one · 2012Article
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Authors and funding
2 authors.
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Abstract
This paper presents an empirical study that aims to explain the relationship between the number of samples and stability of different gene selection techniques for microarray datasets. Unlike other similar studies where number of genes in a ranked gene list is variable, this study uses an alternative approach where stability is observed at different number of samples that are used for gene selection. Three different metrics of stability, including a novel metric in bioinformatics, were used to estimate the stability of the ranked gene lists. Results of this study demonstrate that the univariate selection methods produce significantly more stable ranked gene lists than the multivariate selection methods used in this study. More specifically, thousands of samples are needed for these multivariate selection methods to achieve the same level of stability any given univariate selection method can achieve with only hundreds.
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