3.Conference Paper (09)

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/3908

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Author: search.filters.author.Vijaya, M SEnd date: 2018

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    IDENTIFICATION OF SUBGROUPS IN A DIRECTED SOCIAL NETWORK USING EDGE BETWEENNESS AND RANDOM WALKS
    (Springer Link, 2018) Sathiyakumari, K; Vijaya, M S
    Social networks have obtained masses hobby recently, largely because of the success of online social networking Web sites and media sharing sites. In such networks, rigorous and complex interactions occur among several unique entities, leading to huge information networks with first rate commercial enterprise ability. Network detection is an unmanaged getting to know challenge that determines the community groups based on common place hobbies, career, modules, and their hierarchical agency, the usage of the records encoded in the graph topology. Locating groups from social network is a tough mission because of its topology and overlapping of various communities. In this research, edge betweenness modularity and random walks is used for detecting groups in networks with node attributes. The twitter data of the famous cricket player is used here and network of friends and followers is analyzed using two algorithms based on edge betweenness and random walks. Also the strength of extracted communities is evaluated using on modularity score and the experiment results confirmed that the cricket player’s network is dense.
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    PREDICTING BINDING AFFINITY BASED ON DOCKING MEASURES FOR SPINOCEREBELLAR ATAXIA: A STUDY
    (Springer Link, 2018) Asha, P R; Vijaya, M S
    An obsessive stipulation impairs the regular function or structure of an organ in humans. Spinocerebellar ataxia disorder is a hereditary genetic disorder which is originated by the massive number of sequence variants found in large sets of genes. The mutation in the genes causes many of these disorders. There are certainly no effective drugs to treat those disorders. There are many types of spinocerebellar ataxia, and a better knowledge is required to forecast binding affinity. Binding affinity is crucial to screen the drugs for spinocerebellar ataxia disorder. Accurate identification of binding affinities is a profoundly demanding task. To overcome this issue, a new approach is to be designed in identifying the binding affinity effectively. Due to rapid growth of biological data, there is an increase in the processing time and cost efficiency. This paves the way for challenges in computing. The purpose of machine learning is to excavate beneficial knowledge in distinct to corpus of information and data by constructing effective feasible designs. In this paper, a preface to spinocerebellar ataxia, conventional and innovative strategies involved in predicting binding affinity are discussed.
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    DECISION TREE BASED MODEL FOR THE CLASSIFICATION OF PATHOGENIC GENE SEQUENCES CAUSING ASD
    (Springer Link, 2018-08-21) Pream Sudha, V; Vijaya, M S
    Pathogenic gene identification is an important research problem in biomedical domain. The genetic cause of ASD, which is a multifaceted developmental disability is hard to research. Hence, there is a critical need for inventive approaches to further portray the genetic basis of ASD which will enable better filtering and specific therapies. This paper adopts machine learning techniques to classify gene sequences which are the significant drivers of syndromic and asyndromic ASD. The synthetic dataset with 150 sequences of six different categories of genes were prepared and coding measures of gene sequences were taken as attributes for gene identification. Pattern learning algorithms like support vector machine, decision tree and Multiplayer perceptron were used to train the model. The model was evaluated using 10 fold cross validation and the results are reported. The study reveals that Decision trees outperform other classifiers with an accuracy of 97.33%
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    BINDING AFFINITY PREDICTION MODELS FOR SPINOCEREBELLAR ATAXIA USING SUPERVISED LEARNING
    (Springer Link, 2018-08-21) Asha, P R; Vijaya, M S
    Spinocerebellar Ataxia (SCA) is an inherited disorder flow in the family, even when one parent is affected. Disorder arises mainly due to mutations in the gene, which affects the gray matter in the brain and causes neuron degeneration. There are certain types of SCA that are caused by repeat mutation in the gene, which produces differences in the formation of protein sequence and structures. Binding affinity is essential to know how tightly the ligand binds to the protein. In this work, the binding affinity prediction model is built using machine learning. To build the model, features like Binding energy, IC50, Torsional energy and surface area for both ligand and protein are extracted from Auto dock, auto dock vina and PYmol from the complex. A total of 17 structures and 18 drugs were used for building the model. This paper proposes a predictive model using applied mathematics, machine learning regression techniques like rectilinear regression, Artificial neural network (ANN) and Random Forest (RF). Experimental results show that the model built using Random Forest outperforms in predicting the binding affinity.