scholarly journals Microarray missing values imputation methods: Critical analysis review

2009 ◽  
Vol 6 (2) ◽  
pp. 165-190 ◽  
Author(s):  
Mou'ath Hourani ◽  
Emary El
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Missing Values ◽  
Gene Array ◽  
Estimation Method ◽  
Least Square ◽  
Support Vector ◽  
Expression Data ◽  
Imputation Methods ◽  
Value Estimation

Gene expression data often contain missing expression values. For the purpose of conducting an effective clustering analysis and since many algorithms for gene expression data analysis require a complete matrix of gene array values, choosing the most effective missing value estimation method is necessary. In this paper, the most commonly used imputation methods from literature are critically reviewed and analyzed to explain the proper use, weakness and point the observations on each published method. From the conducted analysis, we conclude that the Local Least Square (LLS) and Support Vector Regression (SVR) algorithms have achieved the best performances. SVR can be considered as a complement algorithm for LLS especially when applied to noisy data. However, both algorithms suffer from some deficiencies presented in choosing the value of Number of Selected Genes (K) and the appropriate kernel function. To overcome these drawbacks, the need for new method that automatically chooses the parameters of the function and it also has an appropriate computational complexity is imperative.

scholarly journals Sparse Representation for Classification of Tumors Using Gene Expression Data

2009 ◽  
Vol 2009 ◽  
pp. 1-6 ◽  
Author(s):  
Xiyi Hang ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Sparse Representation ◽  
Gene Expression Data ◽  
Least Square Method ◽  
Classification Problem ◽  
Least Square ◽  
Support Vector ◽  
Expression Data ◽  
Classification Of Tumors

Personalized drug design requires the classification of cancer patients as accurate as possible. With advances in genome sequencing and microarray technology, a large amount of gene expression data has been and will continuously be produced from various cancerous patients. Such cancer-alerted gene expression data allows us to classify tumors at the genomewide level. However, cancer-alerted gene expression datasets typically have much more number of genes (features) than that of samples (patients), which imposes a challenge for classification of tumors. In this paper, a new method is proposed for cancer diagnosis using gene expression data by casting the classification problem as finding sparse representations of test samples with respect to training samples. The sparse representation is computed by thel1-regularized least square method. To investigate its performance, the proposed method is applied to six tumor gene expression datasets and compared with various support vector machine (SVM) methods. The experimental results have shown that the performance of the proposed method is comparable with or better than those of SVMs. In addition, the proposed method is more efficient than SVMs as it has no need of model selection.

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

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Sujay Saha ◽  
Anupam Ghosh ◽  
Dibyendu Bikash Seal ◽  
Kashi Nath Dey
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Missing Values ◽  
Breast Cancer Dataset ◽  
Expression Data ◽  
Cancer Dataset ◽  
Gene Ranking ◽  
Missing Value ◽  
Value Estimation ◽  
Missing Value Estimation

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.

Missing-Values Imputation Algorithms for Microarray Gene Expression Data

2019 ◽  
pp. 255-266 ◽  
Author(s):  
Kohbalan Moorthy ◽  
Aws Naser Jaber ◽  
Mohd Arfian Ismail ◽  
Ferda Ernawan ◽  
Mohd Saberi Mohamad ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Missing Values ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Microarray Gene
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