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Biclustering deals with creating a sub-matrix that shows a high similarity across both genes and conditions. Biclustering targets at identifying several biclusters that reveal potential local patterns from a microarray matrix. In this paper, initially sequential evolutionary algorithm (SEBI) is implemented and few drawbacks of the approach were identified. To overcome the drawbacks, parallel strategies...
In the context of gene expression microarray data, biclustering is a technique to identify clusters of genes that are co-expressed under clusters of conditions. It usually has high computational complexity (NP-Hard). In computer science domain, combinatorial problems like biclustering, refer to the tasks associated with the discovery of grouping, ordering or assigning a discrete set of objects fulfilling...
Conventional Biclustering methods give critical information regarding correlated patterns in gene expression patterns with similar expressions. These data mining techniques are considered to be the crucial in microarray data analysis. These kinds of data analysis always put focus to the ideal functions of genes and transcription factor target relationships. However by applying traditional clustering...
Bi-clustering of gene expression microarray data deals with creating a sub-matrix that shows a high similarity across both genes and conditions. Bi-clustering aims at identifying several bi-clusters that reveal potential local patterns from a microarray matrix. In this paper, evolutionary algorithm is used to find bi-clusters of large size which have mean squared residue less than a given threshold,...
Microarrays store gene expression data of each cell; thereby microarray contains thousands of features. Each row represents gene samples and each column represents conditions. In any classification task, selection of irrelevant or redundant features greatly reduces the performance of classifier. Therefore, selection of optimal number of significant features is a major challenge for any classification...
Microarrays help in storing gene expression data from a cell. Each microarray describes features of each cell. The rows in microarray represent the samples and the columns represent the gene expression level of the cell. Microarray data is of high dimension due to which classification using conventional methods becomes tedious and inefficient. Therefore, reducing the dimension of long feature vector...
This paper intends to implement Probabilistic Neural Network(PNN) for protein superfamily classification problem. The classification task organizes proteins into their superfamilies and helps in correct prediction of structure and function of newly discovered proteins. The two main steps for any pattern classification problem are feature selection and feature extraction. The bi-gram hashing function...
A protein superfamily consists of proteins which share amino acid sequence homology and are therefore functionally and structurally related. Generally, two proteins are classified into the same class if they have most of the features extracted in common. As the size of the protein databases are becoming larger in size, it is better to develop an intelligent system to classify the protein with high...
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