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Authors
Affiliations
1 CSE Department, Arulmigu Meenakshi Amman College of Engineering, Thiruvannamalai Dt, Near Kanchipuram, IN
2 CSE Department, Ganadipathy Tulsi’s Jain Engineering College, Vellore, IN
3 CSE Department, Arulmigu Meenakshi Amman
College of Engineering, Thiruvannamalai Dt, Near Kanchipuram, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 12 (2010), Pagination:
Abstract
In this work we present a novel approach that uses interspecies sequences homology to connect the networks of multi species and possible more species and possible more species together with gene ontology dependencies in order to improve protein classification for research work. Proteins are involved in many for all biological process such energy metabolism, signal transduction and translation initiation. Even though for a large portion of proteins and their biological function are unknown or incomplete, therefore constructing efficient and reliable models for predicting the protein function has to be used in research work. Our method readily extends to multi species food and produce the improvements similar to them multi species. In the presence of multi interacting networks are using data mining for integration of a data from various sources and contributing increased accuracy of the function prediction of the multiple species for research work. We further enhance our model to account for the gene ontology dependencies by linking multiple related ontology categories such as, we have selected the food items from various countries such as from America the famous food items of yoghurt and Australia food items of oats and Indian food items of soya bean. The data sets are highly desirable for this use from various countries using logical networks from center for bioinformatics research institute (Chennai) and stored in the mining. SFLA aims to set a generic paradism of the efficient mining that acquire the data set of proteins for these food items and promotes predictions of protein functions with gene ontology for research work.
Keywords
Biology and Genetics, Machine Learning, Bioinformatics (Genome or Protein) Databases.
