![](/images/graphics-bg.png)
Multiple Suboptimal Solutions for Prediction Rules in Gene Expression Data
Joint Authors
Eguchi, Shinto
Pritchard, Mari
Komori, Osamu
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-04-16
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
This paper discusses mathematical and statistical aspects in analysis methods applied to microarray gene expressions.
We focus on pattern recognition to extract informative features embedded in the data for prediction of phenotypes.
It has been pointed out that there are severely difficult problems due to the unbalance in the number of observed genes compared with the number of observed subjects.
We make a reanalysis of microarray gene expression published data to detect many other gene sets with almost the same performance.
We conclude in the current stage that it is not possible to extract only informative genes with high performance in the all observed genes.
We investigate the reason why this difficulty still exists even though there are actively proposed analysis methods and learning algorithms in statistical machine learning approaches.
We focus on the mutual coherence or the absolute value of the Pearson correlations between two genes and describe the distributions of the correlation for the selected set of genes and the total set.
We show that the problem of finding informative genes in high dimensional data is ill-posed and that the difficulty is closely related with the mutual coherence.
American Psychological Association (APA)
Komori, Osamu& Pritchard, Mari& Eguchi, Shinto. 2013. Multiple Suboptimal Solutions for Prediction Rules in Gene Expression Data. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-14.
https://search.emarefa.net/detail/BIM-499000
Modern Language Association (MLA)
Komori, Osamu…[et al.]. Multiple Suboptimal Solutions for Prediction Rules in Gene Expression Data. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-14.
https://search.emarefa.net/detail/BIM-499000
American Medical Association (AMA)
Komori, Osamu& Pritchard, Mari& Eguchi, Shinto. Multiple Suboptimal Solutions for Prediction Rules in Gene Expression Data. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-14.
https://search.emarefa.net/detail/BIM-499000
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-499000