Biomedical Research

The sequence of protein comparison and classification is increasing hugely in current era of ‘omics’ revolution. The functionality of proteomics and genomics are creating a large prediction over protein functionality. Query based protein sequence information retrieval from protein Extensible Markup Language (XML) dataset is a challenging scenario in a real world application. The major problem that rises in medical information retrieval is that it cannot retrieve the exact protein content or its ingredients information. Also in retrieving such information the computation cost finds to be much higher and also retrieval accuracy about the particular protein sequence is very minimal. Apart from this another major issue focused is of the communication loss which happens when users communicating with protein Extensible Markup Language (XML) dataset for information retrieval. In order to overcome the above mentioned issues here proposed a new method which resolves the complexity of communication loss of protein Extensible Markup Language (XML) dataset and to retrieve exact protein content or its ingredients information with minimum computation cost. The proposed new method which is of an enhanced B+ tree indexing algorithm helps in retrieving the protein information from protein Extensible Markup Language (XML) dataset with minimal computation cost and accurate result pattern. The proposed approach is designed in such a way to extract the perfect protein similarity matrix from the protein sequence that is extracted from the protein Extensible Markup Language (XML) dataset. All the similarity matrices of protein sequence are generated and collected by the collection of sequence of protein based on users query request. Thus Experimental result show how efficient the proposed enhanced B+ tree indexing algorithm helps in retrieving the protein information from protein Extensible Markup Language (XML) dataset with minimal computation cost and accurate result pattern.

Author(s): Mary Posonia A, Jyothi VL
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