scholarly journals An Improved Kernel Credal Classification Algorithm Based on Regularized Mahalanobis Distance: Application to Microarray Data Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Khawla EL bendadi ◽  
Yissam Lakhdar ◽  
El Hassan Sbai
Keyword(s):  
Microarray Data ◽  
Mahalanobis Distance ◽  
Euclidean Distance ◽  
Synthetic Data ◽  
Classification Algorithm ◽  
Microarray Data Analysis ◽  
Dna Microarray Data ◽  
Real World Application ◽  
Benchmark Database ◽  
Leukemia Dataset

Within the kernel methods, an improved kernel credal classification algorithm (KCCR) has been proposed. The KCCR algorithm uses the Euclidean distance in the kernel function. In this article, we propose to replace the Euclidean distance in the kernel with a regularized Mahalanobis metric. The Mahalanobis distance takes into account the dispersion of the data and the correlation between the variables. It differs from Euclidean distance in that it considers the variance and correlation of the dataset. The robustness of the method is tested using synthetic data and a benchmark database. Finally, a set of DNA microarray data from Leukemia dataset was used to show the performance of our method on real-world application.

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

2003 ◽  
Vol 01 (03) ◽  
pp. 541-586 ◽  
Author(s):  
Tero Aittokallio ◽  
Markus Kurki ◽  
Olli Nevalainen ◽  
Tuomas Nikula ◽  
Anne West ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Biological Research ◽  
Microarray Experiments ◽  
Dna Microarray Data ◽  
Open Questions ◽  
Analysis Technique ◽  
Wide Range

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

Biclustering of DNA Microarray Data

2013 ◽  
pp. 513-551 ◽  
Author(s):  
Alain B. Tchagang ◽  
Youlian Pan ◽  
Fazel Famili ◽  
Ahmed H. Tewfik ◽  
Panayiotis V. Benos
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Dna Microarray ◽  
High Throughput ◽  
Microarray Data ◽  
Evaluation Methods ◽  
Microarray Data Analysis ◽  
Dna Microarray Data ◽  
Daunting Task

In this chapter, different methods and applications of biclustering algorithms to DNA microarray data analysis that have been developed in recent years are discussed and compared. Identification of biological significant clusters of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. Mathematical aspects of the models are highlighted, and applications to solve biological problems are discussed.

Identification of target genes conferring ethanol stress tolerance to Saccharomyces cerevisiae based on DNA microarray data analysis

2007 ◽  
Vol 131 (1) ◽  
pp. 34-44 ◽  
Author(s):  
Takashi Hirasawa ◽  
Katsunori Yoshikawa ◽  
Yuki Nakakura ◽  
Keisuke Nagahisa ◽  
Chikara Furusawa ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Data Analysis ◽  
Stress Tolerance ◽  
Dna Microarray ◽  
Microarray Data ◽  
Target Genes ◽  
Microarray Data Analysis ◽  
Ethanol Stress ◽  
Dna Microarray Data
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