International Journals
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Item PIONEERING METHODS FOR ENHANCING PPI AND PHENOTYPE NETWORKS FOR CANDIDATE DISEASE PRIORITIZATION(International Journal of Engineering and Advanced Technology - Blue Eyes Intelligence Engineering & Sciences Publication, 2019-12) J, Maria Shyla; M, Renuka DeviThe physical contacts of high-specificity between two or more protein molecules constitute Protein-Protein Interactions (PPIs). PPI networks are modeled through graphs where node denotes proteins and edges denote interaction between proteins. The PPI network plays an important role to identify the interesting disease gene candidates. But, the PPI network usually contains false interactions. Many techniques have been proposed to reconstruct PPI network to remove false interactions and improve ranking of candidate disease. Random Walk with Restart on Diffusion profile (RWRDP) and Random Walk on a Reliable Heterogeneous Network (RWRHN) was two among them. In these methods, Gene topological similarity was incorporated with original PPI network to reconstruct new PPI network. Phenotype network was constructed by calculating similarity between gene phenotypes. The reconstructed network and phenotype networks were combined to rank candidate disease genes. However, the PPI reconstruction was fully related with the quality of protein interaction data. In order to enhance the reconstruction of PPI, a Piecewise Linear Regression (PLR) based protein sequence similarity measure and Bat Algorithm based gene expression similarity were proposed with RHN. In this paper, additional measure called Interaction Level Sub cellular Localization Score (ILSLS) is proposed to further reduce the false interaction in the reconstruction of PPI network. ILSLS is the combination of Normalized Sub cellular Localization score (NSL) and Protein Multiple Location Prediction score (PMLP). The proposed work is named as Random Walker on Optimized Trustworthy Heterogeneous Sub Cellular localization aware Network (RW-OTHSN). In order to enhance the ranking of RWOTHSN, phenotype structure is considered while construction phenotype network to rank the candidate disease genes. The phenotype structure is characterized based on h*-sequence model which identify highly discriminative signatures with only a small number of genes. This proposed work is named as Random Walker on Optimized Trustworthy Heterogeneous Sub Cellular localization and Phenotype structure aware Network (RWOTHSPN). The efficiency of the proposed methods are evaluated on PPI network database in terms of Average degree, Relative Frequency for PPI reconstruction, Number of successful predictions, precision and recall for candidate disease gene ranking.Item PRIORITIZATION OF CANDIDATE GENE ASSOCIATED WITH DISEASES IMPROVED BY RANDOM WALKER ON OPTIMIZED TRUSTWORTHY HETEROGENEOUS NETWORK(Jour of Adv Research in Dynamical & Control Systems, 2019-04) J, Maria Shyla; M, Renuka DeviCandidate gene associated with diseases could be ranked by the reconstruction of PPI Network. In current biomedical research, the prioritization of candidate gene is the most essential issue. A reliable heterogeneous network was used for candidate gene prioritization for diseases. This network was constructed by fusion of reconstructed Protein-Protein Interaction (PPI) network by topological similarity, relationship between diseases and genes of proteins and phenotype similarity network. Then, the candidate genes were prioritized by Random Walker on the Reliable Heterogeneous Network (RWRHN) which is a random walk-based algorithm. PPI network reconstruction by protein characteristic further improved the prediction accuracy of disease genes. In this paper, the prioritization of candidate gene for diseases is further improved by proposed Random Walker on Optimized Trustworthy Heterogeneous Network (RW-OTHN) which additionally considering the protein sequence similarity and gene expression profile similarity while reconstructing PPI network. The protein sequence similarity is calculated by piecewise linear regression model. The gene expression profile similarity is calculated by applying sub space clustering on high dimensional gene expression profile data. The subspace clustering is processed by multi objective BAT algorithm and K means clustering. The prioritization of candidate gene is improved with the consideration of protein sequence similarity and gene expression profile similarity in PPI network reconstructionItem ANALYSIS OF VARIOUS DATA MINING TECHNIQUES TO PREDICT DIABETES MELLITUS(Research India Publications, 2016-01) J, Maria Shyla; M, Renuka DeviData mining approach helps to diagnose patient’s diseases. Diabetes Mellitus is a chronic disease to affect various organs of the human body. Early prediction can save human life and can take control over the diseases. This paper explores the early prediction of diabetes using various data mining techniques. The dataset has taken 768 instances from PIMA Indian Dataset to determine the accuracy of the data mining techniques in prediction. The analysis proves that Modified J48 Classifier provide the highest accuracy than other techniques.