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This paper deals with the problem of mining acronyms and their definitions from biomedical text. We propose an effective text mining system by using pattern matching method. Different stages of the design have been explained with pseudo code. We used space reduction heuristic constraints (D. Nadeau and P. Turney, 2005) which will increase the precision by(More)
Clustering is one of the widely used unsupervised methods to interpret and analyze huge amount of data in the field of Bioinformatics. One of the major issues involved in clustering is to address the growing data so that the cluster quality does not decrease with increase in the size of the data. In this work, we compare the promising clustering algorithms(More)
The bioinformatics field which is now dealing with a vast amount of data such as the protein patterns and the gene expression data, with a lot more information still to be unraveled, uses the basic techniques and tools for Data mining for retrieving useful information from huge biological databases. Clustering is a popular Data mining technique which is(More)
Hepatitis C Virus (HCV) has become a major risk factor for the development of Hepatocellular Carcinoma (HCC). A framework has been developed to identify genomic markers associated with HCC of HCV sequences, which comprises of clustering, feature selection and classification. A new method for feature extraction for genomic sequences rooted in Hash tables has(More)
Identification of structural and sequence motifs in genomic sequences is gaining much attention now a days. Ribonucleic acid or RNA is one of the important biomolecule whose secondary structure defines its functionality. Soft computing techniques like genetic programming have been used for motif identification. In this paper, we propose a method for(More)
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