Shubhra Sankar Ray

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This paper deals with some new operators of genetic algorithms and[-27pc] demonstrates their effectiveness to the traveling salesman problem (TSP) and microarray gene ordering. The new operators developed are nearest fragment operator based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. While these result(More)
This paper describes an application of genetic algorithm to the traveling salesman problem. New knowledge based multiple inversion operator and a neighborhood swapping operator are proposed. Experimental results on different benchmark data sets have been found to provide superior results as compared to some other existing methods.
MicroRNAs (miRNAs) play a major role in cancer development and also act as a key factor in many other diseases. In this investigation, we propose three methods for handling miRNA expressions. The first two methods determine whether a miRNA is indicating normal or cancer condition, and the third one determines how many miRNAs are supporting the cancer(More)
MOTIVATION One of the important goals of biological investigation is to predict the function of unclassified gene. Although there is a rich literature on multi data source integration for gene function prediction, there is hardly any similar work in the framework of data source weighting using functional annotations of classified genes. In this(More)
MicroRNAs (miRNA) are one kind of non-coding RNA which play many important roles in eukaryotic cell. Investigations on miRNAs show that miRNAs are involved in cancer development in animal body. In this article, a threshold based method to check the condition (normal or cancer) of miRNAs of a given sample/patient, using weighted average distance between the(More)
A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering and ordering the genes using gene expression data into homogeneous groups was shown to be useful in functional annotation, tissue classification, regulatory motif identification, and other applications.(More)
This paper deals with some new operators of genetic algorithms for solving the traveling salesman problem (TSP). These include a new operator called, " nearest fragment operator " based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. Superiority of these operators has been established on different benchmark(More)
A granular neural network for identifying salient features of data, based on the concepts of fuzzy set and a newly defined fuzzy rough set, is proposed. The formation of the network mainly involves an input vector, initial connection weights and a target value. Each feature of the data is normalized between 0 and 1 and used to develop granulation structures(More)
Keywords: Fuzzy rough sets Rule based layered network Fuzzy reflexive relation Unsupervised learning Cluster validity index Natural computing Microarray Gene-function a b s t r a c t A fuzzy rough granular self-organizing map (FRGSOM) involving a 3-dimensional linguistic vector and connection weights, defined in an unsupervised manner, is proposed for(More)