Open AccessPartial Context Similarity of Gene/Proteins in Leukemia Using Context Rank Based Hierarchical Clustering Algorithm
Author(s) -
Shahina Bano,
K.Rajasekara Rao
Publication year - 2015
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v5i3.pp483-490
Subject(s) - computer science , cluster analysis , context (archaeology) , hierarchical clustering , overhead (engineering) , parsing , identification (biology) , data mining , similarity (geometry) , rank (graph theory) , artificial intelligence , probabilistic logic , pattern recognition (psychology) , algorithm , mathematics , paleontology , botany , combinatorics , image (mathematics) , biology , operating system
In this paper we proposed a method which avoids the choice of natural language processing tools such as pos taggers and parsers reduce the processing overhead. Moreover, we suggest a structure to immediately create a large-scale corpus annotated along with disease names, which can be applied to train our probabilistic model. In this proposed work context rank based hierarchical clustering method is applied on different datasets namely colon, Leukemia, MLL medical diseases. Optimal rule filtering algorithm is applied on these datasets to remove unwanted special characters for gene/protein identification. Finally, experimental results show that proposed method outperformed existing methods in terms of time and clusters space.