Monalisa Mandal

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The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is organized as a graph-theoretic problem where a feature-dissimilarity graph is shaped from the data matrix. The nodes represent features and the edges represent their dissimilarity. Both nodes and edges are(More)
Cancer is a phenotypic complexity which affects genes, proteins, pathways and regulatory networks. The research is still in progress to identify the important genes which are responsible for various types of cancer. In this context important genes refers to the gene marker which indicates change in expression or state of protein that correlates with the(More)
In this article, the possible subcellular location of a protein is predicted using multiobjective particle swarm optimization-based feature selection technique. In general form of pseudo-amino acid composition, the protein sequences are used for constructing protein features. Here, the different amino acids compositions are used to construct the feature(More)
Identifying relevant genes which are responsible for various types of cancer is an important problem. In this context, important genes refer to the marker genes which change their expression level in correlation with the risk or progression of a disease, or with the susceptibility of the disease to a given treatment. Gene expression profiling by microarray(More)
In this article, a new and robust pathway activity inference scheme is proposed from gene expression data using Particle Swarm Optimization (PSO). From microarray gene expression data, the corresponding pathway information of the genes are collected from a public database. For identifying the pathway markers, the expression values of each pathway consisting(More)
In this article, the probable sub cellular location of a protein is predicted by applying multiobjective particle swarm optimization (MOPSO) based feature selection technique. The feature set is created from the different amino acid compositions of the protein. Thus, the sample of protein versus amino acid compositions (features) constitutes the dataset.(More)