f) 2019-Scopus Open Access (PDF)

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Author: search.filters.author.Vijaya, M S

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    AFFINITY PREDICTION USING MUTATED PROTEIN-LIGAND DOCKING WITH REGRESSION TECHNIQUES OF SCA
    (Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP), 2019-07) Asha, P R; Vijaya, M S
    Drug discovery for rare genetic disorder like spinocerebellar ataxia is very complicated in biomedical research. Numerous approaches are available for drug design in clinical labs, but it is time consuming. There is a need for affinity prediction of spinocerebellar ataxia, which will help in facilitating the drug design. In this work, the proteins are mutated with the information available from HGMD database. The repeat mutations are induced manually, and that mutated proteins are docked with ligand. The model is trained with extricated features such as energy profiles, rf-score, autodock vina scores, cyscore and sequence descriptors. Regression techniques like linear, polynomial, ridge, SVM and neural network regression are implemented. The predictive models are built with various regression techniques and the predictive model implemented with support vector regression is compared with support vector regression kernel. Among all regression techniques, SVR performs well than the other regression models.
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    ANFIS FOR TAMIL PHONEME CLASSIFICATION
    (Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP), 2019-08) Laxmi Sree, B R; Vijaya, M S
    Phoneme recognition is an intricate problem lying under non-linear systems. Most research in this area revolve around try to model the pattern of features observed in the speech spectra with the use of Hidden Markov Models (HMM), various types of neural networks like deep recurrent neural networks, time delay neural networks, etc. for efficient phoneme recognition. In this paper, we study the effectiveness of the hybrid architecture, the Adaptive Neuro-Fuzzy Inference System (ANFIS) for capturing the spectral features of the speech signal to handle the problem of Phoneme Recognition. In spite of a wide range of research in this field, here we examine the power of ANFIS for least explored Tamil phoneme recognition problem. The experimental results have shown the ability of the model to learn the patterns associated with various phonetic classes, indicated with recognition improvement in terms of accuracy to its counterparts.
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    DEEP LEARNING BASED PREDICTION OF AUTISM SPECTRUM DISORDER USING CODON ENCODING OF GENE SEQUENCES
    (Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP), 2019-10) Pream Sudha, V; Vijaya, M S
    The development of computational tools to recognize Autism Spectrum Disorder (ASD) originated by genetic mutations is vital to the development of disease-specific targeted therapies. Identifying genes causing the genetically transmitted ASD is still a challenging task. As genomics data is dependent on domain specific experts for identifying efficient features and extracting hand-crafted attributes involves much time, an alternate effective solution is the need of the hour. The rapid developments in the design of deep architecture models have led to the broad application of these models in a variety of research areas and they have shown considerable success in sequential data processing tasks. The primary goal of this work is to classify the ASD gene sequences by employing a Deep Neural Network based model. This in turn will enable effective genetic diagnoses of this disease and facilitate the targeted genetic testing of individuals. This work utilizes codon encoding and one hot encoding technique to transform the mutated gene sequences which are exploited for self learning the features by deep network. Experiments showed that the performance of the proposed model was better than that of the conventional Multilayer Perceptron with promising accuracy of 77.8%, 80.1% and 81.2% for three different datasets.