An Improved Fuzzy Based Missing Value Estimation in DNA Microarray Validated by Gene Ranking

Most of the gene expression data analysis algorithms require the entire gene expression matrix without any missing values. Hence, it is necessary to devise methods which would impute missing data values accurately. There exist a number of imputation algorithms to estimate those missing values. This...

Full description

Saved in:
Bibliographic Details
Published in:Advances in fuzzy systems 2016-01, Vol.2016 (2016), p.1-19
Main Authors: Dey, Kashi Nath, Seal, Dibyendu Bikash, Ghosh, Anupam, Saha, Sujay
Format: Article
Language:English
Subjects:
Algorithms
Breast cancer
Classification
Colleges & universities
Computer science
Datasets
Deoxyribonucleic acid
DNA
Engineering
Gene expression
Leukemia
Mean square errors
Methods
Prostate cancer
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Most of the gene expression data analysis algorithms require the entire gene expression matrix without any missing values. Hence, it is necessary to devise methods which would impute missing data values accurately. There exist a number of imputation algorithms to estimate those missing values. This work starts with a microarray dataset containing multiple missing values. We first apply the modified version of the fuzzy theory based existing method LRFDVImpute to impute multiple missing values of time series gene expression data and then validate the result of imputation by genetic algorithm (GA) based gene ranking methodology along with some regular statistical validation techniques, like RMSE method. Gene ranking, as far as our knowledge, has not been used yet to validate the result of missing value estimation. Firstly, the proposed method has been tested on the very popular Spellman dataset and results show that error margins have been drastically reduced compared to some previous works, which indirectly validates the statistical significance of the proposed method. Then it has been applied on four other 2-class benchmark datasets, like Colorectal Cancer tumours dataset (GDS4382), Breast Cancer dataset (GSE349-350), Prostate Cancer dataset, and DLBCL-FL (Leukaemia) for both missing value estimation and ranking the genes, and the results show that the proposed method can reach 100% classification accuracy with very few dominant genes, which indirectly validates the biological significance of the proposed method.
ISSN:1687-7101
1687-711X
DOI:10.1155/2016/6134736