Feature Selection Approach in Microarray Data Processing


What is Microarray Technology?

-- The microarray technology usually uses the sequence of resources those are constructed through various genome projects. Additional sequencing efforts are needed in order to monitor the expression of huge numbers of genes. Microarray can be defined as a glass slide on which single-stranded DNA molecules are associated at constant regions. These regions are known as spots. There are large numbers of spots on a single microarray. Every individual microarray includes large numbers of identical DNA molecules those are responsible for the identification of a particular gene. Different hybridization experiments along with two numbers of samples have the following phases:- In the initial phase, all mRNA from cells include two numbers of conditions. The healthy and cancerous cells are dyed with different fluorescent labels. In the subsequent phase, the labelled mRNA is cleaned over the microarray. 


-- All of these gene products are hybridized with the complementary sequences in the spots. When the microarray is excited with the help of a laser, every individual spot emits fluorescence. It has the responsibility to measure the exact quantity of the sample out of different conditions. Among all applications of microarray technology, gene expression in Cancer disease diagnosis is the most popular one. Unlike classical approaches, microarray technology has the responsibility to detect different patterns of normal as well as abnormal tissues more efficiently and effectively. The major advantage of this technology is the efficiency to measure large numbers of genes in a particular experiment.


Feature Selection in Microarray Data Processing:


Microarray data is generally gathered from the genes. Then, it is compared with the numbers of samples. Therefore, most of the traditional approaches detect inappropriate and computationally infeasible data. We can mention here that, all of the genes are not required during the process of classification. Large numbers of genes are not at all relevant and they never influence the classification performance. By considering these mentioned genes, the dimension of the problem increases exponentially. Hence, the overall computational overhead also increases significantly. Unwanted noise is resulted during the process of classification. Hence, it is very much required to select few numbers of genes those usually taking part during the classification process. The above-mentioned genes are also known as informative genes. 


-- Mostly the best subset of genes is always unknown. All of the traditional gene selection techniques involve a perfect combination of filter and wrapper schemes. Filtering approaches have the responsibility to rank every individual feature, according to its goodness. During the process of ranking, the relationship between every individual gene with the respective class label is considered. Univariate scoring metric plays a significant role in the above-ranking process. The top-ranked genes are selected prior to the execution of classification schemes. On the contrary, wrapper schemes need the gene selection approach in order to merge with a classifier. The prime objective of this technique is to evaluate the classification performance of every individual gene subset. 


-- The optimal subset of genes is detected according to the ranking of performance. The filtering scheme is incapable and inefficient to measure the relationship between different genes. On the other hand, the wrapper technique causes huge computational costs. The gene expression data play a significant role during the process of biomedical diagnosis. A microarray instance, includes huge numbers of genes or characteristics. But, the numbers of available microarray instances are restricted. According to the latest research concepts, limited numbers of genes may result high prediction accuracy during the diagnostic process of cancer disease. 


Conclusion:


Large numbers of genes are not relevant to the disease of interest. The expression data of certain genes may include noise in order to degrade the prediction accuracy. Therefore, the gene selection procedure is a very complicated task during microarray data processing. Feature selection is considered as the most powerful tool in order to decrease the size of the available data. This will no doubt enhance the overall classification accuracy, computational efficiency, and the interpretation of learning outcomes.



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