論文

査読有り
2013年

Multiple suboptimal solutions for prediction rules in gene expression data

Computational and Mathematical Methods in Medicine
  • Osamu Komori
  • ,
  • Mari Pritchard
  • ,
  • Shinto Eguchi

2013
開始ページ
14
終了ページ
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1155/2013/798189

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. © 2013 Osamu Komori et al.

リンク情報
DOI
https://doi.org/10.1155/2013/798189
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/23662163
ID情報
  • DOI : 10.1155/2013/798189
  • ISSN : 1748-670X
  • ISSN : 1748-6718
  • PubMed ID : 23662163
  • SCOPUS ID : 84877277233

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