A quasi-linear approach for microarray missing value imputation

Yu Cheng, Lan Wang, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Missing value imputation for microarray data is important for gene expression analysis algorithms, such as clustering, classification and network design. A number of algorithms have been proposed to solve this problem, but most of them are only limited in linear analysis methods, such as including the estimation in the linear combination of other no-missing-value genes. It may result from the fact that microarray data often comprises of huge size of genes with only a small number of observations, and nonlinear regression techniques are prone to overfitting. In this paper, a quasi-linear SVR model is proposed to improve the linear approaches, and it can be explained in a piecewise linear interpolation way. Two real datasets are tested and experimental results show that the quasi-linear approach for missing value imputation outperforms both the linear and nonlinear approaches.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages233-240
Number of pages8
Volume7062 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2011
Event18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai
Duration: 2011 Nov 132011 Nov 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7062 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other18th International Conference on Neural Information Processing, ICONIP 2011
CityShanghai
Period11/11/1311/11/17

Fingerprint

Missing Values
Imputation
Microarrays
Microarray
Genes
Microarray Data
Gene expression
Gene
Gene Expression Analysis
Interpolation
Linear Interpolation
Nonlinear Regression
Overfitting
Network Design
Piecewise Linear
Linear Combination
Clustering
Experimental Results
Model

Keywords

  • microarray data
  • missing value imputation
  • quasi-linear
  • SVR

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Cheng, Y., Wang, L., & Furuzuki, T. (2011). A quasi-linear approach for microarray missing value imputation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 7062 LNCS, pp. 233-240). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7062 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-24955-6_28

A quasi-linear approach for microarray missing value imputation. / Cheng, Yu; Wang, Lan; Furuzuki, Takayuki.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7062 LNCS PART 1. ed. 2011. p. 233-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7062 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Cheng, Y, Wang, L & Furuzuki, T 2011, A quasi-linear approach for microarray missing value imputation. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 7062 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7062 LNCS, pp. 233-240, 18th International Conference on Neural Information Processing, ICONIP 2011, Shanghai, 11/11/13. https://doi.org/10.1007/978-3-642-24955-6_28
Cheng Y, Wang L, Furuzuki T. A quasi-linear approach for microarray missing value imputation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 7062 LNCS. 2011. p. 233-240. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-24955-6_28
Cheng, Yu ; Wang, Lan ; Furuzuki, Takayuki. / A quasi-linear approach for microarray missing value imputation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7062 LNCS PART 1. ed. 2011. pp. 233-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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