Browsing by Author "Asha, P R"

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AFFINITY PREDICTION OF SPINOCEREBELLAR ATAXIA USING PROTEIN-LIGAND AND PROTEIN-PROTEIN INTERACTIONS WITH FUNCTIONAL DEEP LEARNING
(Blue Eyes Intelligence Engineering & Sciences Publication, 2019-06) Asha, P R; Vijaya, M S
Drug discovery of incomparable hereditary disorder like spinocerebellar ataxia is confronted and an enforce task in biomedical study. There are number of paths available for affinity prediction through scoring functions and ideals in the catalog. Nevertheless there is a need for artistic access in portraying the affinity of spinocerebellar ataxia which will facilitate enhanced prediction for drug discovery. This research work portrays the significance of docking for protein-ligand interaction and protein-protein interaction with modeling through deep learning. Deep Neural Networks is utilized in predicting binding affinity with 3d protein structures and ligand. Predictive models have been built with features related to for protein-ligand interaction and protein-protein interaction. In the first case, 17 protein structures and 18 ligands were used. Each protein structure is docked with ligand to get essential features like energy calculations, properties of protein and ligand for predicting binding affinity. In the next case, repeat mutation is induced manually with 17 protein structures and docked with 18 ligands. To train the model, well-defined descriptors are squeezed from the docked complex. Third case employs protein-protein interaction of total of 626 protein structures and the complexes attained from the protein-protein interaction are 313. Features like energy calculations, physio-chemical properties and interfacial and non-interfacial properties are extracted for learning this model. Deep learning has the property of representation learning from the user defined features, which helps in accurate prediction of binding affinity. The predictive models are developed with functional deep neural network and their performances are compared with sequential deep neural network. Functional deep neural network have more flexibility to define layers, complements sequential deep neural network which results in improved performance.
AFFINITY PREDICTION OF SPINOCEREBELLAR ATAXIA USING PROTEIN-PROTEIN INTERACTIONS AND DEEP NEURAL NETWORK WITH USER-DEFINED LAYER
(International Journal of Advanced Science and Technology, 2019) Asha, P R; Vijaya, M S
Binding affinity prediction for a rare genetic disorder like spinocerebellar ataxia is crucial in biomedical study. Numerous models for affinity prediction have been developed through machine learning and deep learning. The basic deep neural network architecture uses a linear stack of layers and sharing of layers is not feasible whereas the functional deep neural network uses sharing of layers but the models are affected, when there is a change in layer. Hence complex models cannot be constructed and cannot predict binding affinity efficiently. This problem can be overcome by customizing the layers in deep neural network architecture. In this research work, the network layers are defined by sharing features with several layers and weights are trained and updated for every iteration to obtain accurate prediction. The work is implemented with 626 protein structures for protein-protein interaction and 313 complexes are attained from the protein-protein interaction. Binding site is identified by passing the 3D protein structures into convolutional neural network. Features like energy calculations, physio-chemical properties and interfacial and non-interfacial properties are extracted from interacted complex for building the model. Feature representations are learned automatically by deep learning through trainable weights in customized layers. Deep neural network with user defined layers is modelled with three optimizers and the results are correlated with functional deep neural network based affinity prediction models. The result shows that the proposed deep neural network with customized layers and adam optimizer achieves the highest prediction rate of 0.98.
AFFINITY PREDICTION USING MUTATED PROTEIN-LIGAND DOCKING WITH REGRESSION TECHNIQUES OF SCA
(Blue Eyes Intelligence Engineering & Sciences Publication, 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.
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.
DEEP NEURAL NETWORKS FOR AFFINITY PREDICTION OF SPINOCEREBELLAR ATAXIA USING PROTEIN STRUCTURES
(Journal of Advanced Research in Dynamical and Control Systems, 2019) Asha, P R; Vijaya, M S
Drug identification for exceptional hereditary disorder like spinocerebellar ataxia (SCA) is defy and an obligatory chore in biomedical investigate. There are numeral paths obtainable for affinity prediction through diverse scores and features in a typical computational scaffold. However there is a need for creative approaches to further depict the affinity of spinocerebellar ataxia which will enable better prediction for drug identification. This research work depicts the importance of hotspots identification for protein-protein interaction and also rigid docking. The power of Deep Neural Networks is utilized to predict binding affinity patterns through 3d protein structures and interactive properties of the interacted complex that can be used for predicting binding affinity. The work is carried out with 626 protein structures to perform protein-protein interaction. The complexes attained from the protein-protein interaction are 313 and from these complexes features are extracted. Features like physio-chemical properties, energy calculations, interfacial and non-interfacial properties are extracted from the complexes to model the binding affinity and a dataset with 313 instances is developed. Deep learning architectures learn complicated patterns, by gradually building from simpler ones. Deep learning models has many layers, when it goes deeper the model gets refined to provide better results. Input is given as feature vectors for the reason that of the docking process. Deep neural network works well for prediction by learning the features and the signals between them and the experiments discovered the dominance of deep learning neural network when compared to traditional ensemble learning.
DIABETIC RETINAL EXUDATES DETECTION USING EXTREME LEARNING MACHINE
(Springer Link, 2015) Asha, P R; Karpagavalli, S
Diabetic Retinopathy is a disorder of the retina as a result of the impact of diabetes on the retinal blood vessels. It is the major cause of blindness in people like age groups between 20 & 60. Since polygenic disorder proceed, the eyesight of a patient may commence to deteriorate and causes blindness. In this proposed work, the existence or lack of retinal exudates are identified using Extreme Learning Machine(ELM). To discover the occurrence of exudates features like Mean, Standard deviation, Centroid and Edge Strength are taken out from Luv color space after segmenting the Retinal image. A total of 100 images were used, out of which 80 images were used for training and 20 images were used for testing. The classification task carried out with classifier extreme learning machine (ELM). An experimental result shows that the model built using Extreme Learning Machine outperforms other two models and effectively detects the presence of exudates in retina.
DIABETIC RETINAL EXUDATES DETECTION USING MACHINE LEARNING TECHNIQUES
(IEEE, 2015-11-12) Asha, P R; Karpagavalli, S
Diabetic Retinopathy (DR) is an eye filled illness caused by the complication of polygenic disease and that is to be detected accurately for timely treatment. As polygenic disease progresses, the vision of a patient could begin to deteriorate and leads to blindness. In this proposed work, the presence or absence of retinal exudates are detected using machine learning (ML) techniques. To detect the presence of exudates features like Mean, Standard deviation, Centroid and Edge Strength are extracted from Luv color space after segmenting the Retinal image. A total of 100 images were used, out of which 80 images were used for training and 20 images were used for testing. The classification task carried out with classifiers like Naive bayes (NB), Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM). Experimental results shows that the model built using Extreme Learning Machine outperforms other two models and effectively detects the presence of exudates in retinal images.
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.
SUPPORT VECTOR REGRESSION FOR PREDICTING BINDING AFFINITY IN SPINOCEREBELLAR ATAXIA
(Springer Link, 2019) Asha, P R; Vijaya, M S
Spinocerebellar ataxia (SCA) is an inherited disorder. It arises mainly due to gene mutations, which affect gray matter in the brain causing neurodegeneration. 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 very essential to know how tightly the ligand binds with the protein. In this work, a binding affinity prediction model is built using machine learning. To build the model, predictor variables and their values such as binding energy, IC50, torsional energy and surface area for both ligand and protein are extracted from the complex using AutoDock, AutoDock Vina and PyMOL. A total of 17 structures and 18 drugs were used for learning the support vector regression (SVR) model. Experimental results proved that the SVR-based affinity prediction model performs better than other regression models.