Suhas A Bhyratae | Visvesvaraya Technological University (original) (raw)
Papers by Suhas A Bhyratae
nternational journal of advanced research in computer and communication engineering, Mar 30, 2019
A vast amount of data is generated in the fields of healthcare and diagnostics, doctors have to m... more A vast amount of data is generated in the fields of healthcare and diagnostics, doctors have to make a direct contact with patients to determine the wounds, injuries and diseases by which the patient is affected. This paper highlights the application of classifying and predicting a specific disease by implementing the operations on medical data generated in the field of medical and healthcare. The proposed system can solve difficult queries for detecting a particular disease and also can assist medical practitioners to make smart clinical decisions which traditional decision support systems were not able to. The decisions taken by medical practitioners with the help of technology can result in effective and low cost treatments. In this paper, data mining methods namely, Naive Bayes and J48 algorithms are compared for testing their accuracy and performance on the training medical datasets.
Biomedical Engineering: Applications, Basis and Communications
Gene Regulatory Networks (GRNs) reconstruction aims to infer relationships of potential regulatio... more Gene Regulatory Networks (GRNs) reconstruction aims to infer relationships of potential regulation among the genes. With the rapid growth of the biotechnology, such as Ribonucleic acid (RNA)-sequencing and gene chip microarray, the generated high-throughput data provide gene–gene interaction relationships with more opportunities based on gene expression data. Several approaches are introduced to reconstruct the GRNs, but low accuracy is a major drawback. Hence, this paper introduces the hybrid distance measure and the Pearson’s correlation coefficient for reconstructing GRN. The hybrid distance, such as Tversky index, Tanimoto similarity, and Minkowski distance, is employed to connect the edges. The asymmetric partial correlation network is introduced for determining two influence functions for every pair, and edge direction is determined among them. However, the direction of edges is unknown usually and seems difficult to be identified based on gene expression data. Thus, it extend...
International Journal for Research in Applied Science and Engineering Technology
Molecular networks involve interacting proteins, RNA, and DNA molecules, which underlie the major... more Molecular networks involve interacting proteins, RNA, and DNA molecules, which underlie the major functions of living cells. DNA microarray probes how the gene expression changes to perform complex coordinated tasks in adaptation to a changing environment at a genome-wide scale. Microarray is a technology that has been widely used to probe the presence of genes in a sample of DNA or RNA. This technology helps to check the expression levels of thousands of genes together. The DNA microarray was established as a tool for the efficient collection of mRNA expression for a large number of genes. The mapping function route maps pairs of genes that present similar positive, and negative interactions and also defines how the range of each gene is going to be segmented. From all the combinations a function transforms each pair of labels into another one that classifies the type of interaction. This project addresses the challenge of reconstructing molecular networks and gene regulation from gene expression data. Reconstruction of gene regulatory networks which can also be called reverse engineering is a process of identifying gene interaction networks from the experimental microarray gene expression profiles through computation techniques. The main features involved in the computation of interaction in the filtered genes are the discretization mapping function, gene-gene mapping function, and filtering function.
International Journal for Research in Applied Science and Engineering Technology
Computational molecular biology is the superset of Bioinformatics. Areas such as genomics, mappin... more Computational molecular biology is the superset of Bioinformatics. Areas such as genomics, mapping, sequencing, determination of sequence and structure are applied by computer and data science classically. Computational biology is the application of computational tools and techniques to molecular biology. It is a multidisciplinary area of study that combines biology, computer science and statistics. There are different types of problems in computational biology such as how to recognize genes in DNA sequences that contain regulatory information, to determine interaction and regulation of all genes, to predict the structure of proteins, to forecast the functions of newly discovered proteins, to cluster and classify proteins into families and to align similar proteins etc. computational biology driven by two factors, 1. The recent explosion in the amount of available biological data, 2. Commercial incentive to exploit the available biological data for drug discovery and other developments. The algorithms are computationally expensive and computational patterns range from regular to very irregular structured patterns. This outlines computational issues related to parallelism i.e., parallel programming approaches which involves increasing efficiency and creates accurate outcomes.
IJARCCE
A vast amount of data is generated in the fields of healthcare and diagnostics, doctors have to m... more A vast amount of data is generated in the fields of healthcare and diagnostics, doctors have to make a direct contact with patients to determine the wounds, injuries and diseases by which the patient is affected. This paper highlights the application of classifying and predicting a specific disease by implementing the operations on medical data generated in the field of medical and healthcare. The proposed system can solve difficult queries for detecting a particular disease and also can assist medical practitioners to make smart clinical decisions which traditional decision support systems were not able to. The decisions taken by medical practitioners with the help of technology can result in effective and low cost treatments. In this paper, data mining methods namely, Naive Bayes and J48 algorithms are compared for testing their accuracy and performance on the training medical datasets.
Indian Journal of Science and Technology, 2016
Objectives: The objective of the proposing system is to classify Computed Tomographyimages into t... more Objectives: The objective of the proposing system is to classify Computed Tomographyimages into two classifications like cancernodes and without cancernodes for lung pictures using SVM and PSO classifiers. Methods/Analysis: Computed tomography (CT) cancerous lung images are used. Techniques employed include the usage of support vector machine and particle swarm optimization classifiers. Proposed techniques include histogram equalization, ROI masking using thresholding, wavelet transform, gray level co-occurrence matrix. Hence resulting in results in high accuracy, also yields better result in terms of the recognition accuracy. Findings: SVM shows higher accuracy rate of identifying the cancerous nodules than PSO for different iterations held. These comparison results between SVM and PSO bring out the efficiency of comparing between the two classifications algorithms. In the existing system 1.afusion of classification techniques was used to determine the lung nodes in the CT image and feature extraction based technique was implemented on the node. 2. As the second step, the features were computed based on its texture to distinguish the blood vessels. The proposed system is implemented to classify by combining of SVM and PSO. Hence leading to Low performance and accuracy in classification. With the results obtained in the proposed system it differs in various aspects like the lung nodes are well identified, better result obtained due to comparisons hence highlights the fact that proposed system had far more advantages than the existing system.
2013 7th International Conference on Intelligent Systems and Control (ISCO), 2013
In this paper, we present a systematic and conceptual overview of methods for inferring gene regu... more In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Three different inference methods are used namely, ARACNE, CLR, and MRNET. Further, we generate the network containing the gene-gene interactions and compare the inference methods by calculating their accuracy.
International Journal of Innovative Research in Computer and Communication Engineering, 2014
The DNA microarray has been established as a tool for efficient collection of mRNA expression dat... more The DNA microarray has been established as a tool for efficient collection of mRNA expression data for a large number of genes simultaneously.Mapping function approach maps pairs of genes that present similar positive and/or negative interactions and also specifies how the range of each gene is going to be segmented (labels). From all the label combinations a function transforms each pair of labels into another one, which identifies the type of interaction.
Segmentation of human brain from MRI slices for identification of brain lesions has become one of... more Segmentation of human brain from MRI slices for identification of brain lesions has become one of the most active research areas in the field of medical image processing. Brain segmentation has various important applications in diagnosing a number of disorders. The main aim of this project work is to recognize lesions and its qualifications from a specific MRI scan of a brain image and compute the area of each lesion by thresholding. Thresholding approach segment scalar images by generating a binary partitioning of the image intensities. Otsu’s technique is used to automatically carry out intensity based image thresholding. The quantitative analysis of MRI brain lesions allows obtaining useful key information about the lesions. Segmentation is done on basis of threshold, due to which whole image is converted into binary image.
Gene regulation refers to a number of sequential processes, the most well-known and understood be... more Gene regulation refers to a number of sequential processes, the most well-known and understood being translation and transcription, which control the level of a gene’s expression and ultimately result with specific quantity of a target protein. Reconstruction of gene regulatory networks is a process of analyzing the steps involved in gene regulation using computational techniques. In this paper, cancer-specific gene regulatory network has been reconstructed using information theoretic approach-Mutual Information. The microarray database used contains 12 Gene samples each of breast cancer and prostate cancer having both normal and tumor cell information. This data has been preprocessed, normalized and filtered using the t-test; the MI value is applied on the filtered genes to determine the Gene-Gene Interaction. Based on the interactions obtained, 10 different networks have been constructed and the statistical analysis has been performed on that network. Finally, validation of the in...
ABSTRACT: Gene regulation refers to a number of sequential processes, the most well-known and und... more ABSTRACT: Gene regulation refers to a number of sequential processes, the most well-known and understood being translation and transcription, which control the level of a gene's expression and ultimately result with specific quantity of a target protein. Reconstruction of gene regulatory networks is a process of analyzing the steps involved in gene regulation using computational techniques. In this paper, cancer-specific gene regulatory network has been reconstructed using information theoretic approach-Mutual Information. The microarray database used contains 12 Gene samples each of breast cancer and prostate cancer having both normal and tumor cell information. This data has been preprocessed, normalized and filtered using the t-test; the MI value is applied on the filtered genes to determine the Gene-Gene Interaction. Based on the interactions obtained, 10 different networks have been constructed and the statistical analysis has been performed on that network. Finally, valid...
Computational molecular biology is the superset of Bioinformatics. Areas such as genomics, mappin... more Computational molecular biology is the superset of Bioinformatics. Areas such as genomics, mapping, sequencing, determination of sequence and structure are applied by computer and data science classically. Computational biology is the application of computational tools and techniques to molecular biology. It is a multidisciplinary area of study that combines biology, computer science and statistics. There are different types of problems in computational biology such as how to recognize genes in DNA sequences that contain regulatory information, to determine interaction and regulation of all genes, to predict the structure of proteins, to forecast the functions of newly discovered proteins, to cluster and classify proteins into families and to align similar proteins etc. computational biology driven by two factors, 1. The recent explosion in the amount of available biological data, 2. Commercial incentive to exploit the available biological data for drug discovery and other developments. The algorithms are computationally expensive and computational patterns range from regular to very irregular structured patterns. This outlines computational issues related to parallelism i.e., parallel programming approaches which involves increasing efficiency and creates accurate outcomes.
nternational journal of advanced research in computer and communication engineering, Mar 30, 2019
A vast amount of data is generated in the fields of healthcare and diagnostics, doctors have to m... more A vast amount of data is generated in the fields of healthcare and diagnostics, doctors have to make a direct contact with patients to determine the wounds, injuries and diseases by which the patient is affected. This paper highlights the application of classifying and predicting a specific disease by implementing the operations on medical data generated in the field of medical and healthcare. The proposed system can solve difficult queries for detecting a particular disease and also can assist medical practitioners to make smart clinical decisions which traditional decision support systems were not able to. The decisions taken by medical practitioners with the help of technology can result in effective and low cost treatments. In this paper, data mining methods namely, Naive Bayes and J48 algorithms are compared for testing their accuracy and performance on the training medical datasets.
Biomedical Engineering: Applications, Basis and Communications
Gene Regulatory Networks (GRNs) reconstruction aims to infer relationships of potential regulatio... more Gene Regulatory Networks (GRNs) reconstruction aims to infer relationships of potential regulation among the genes. With the rapid growth of the biotechnology, such as Ribonucleic acid (RNA)-sequencing and gene chip microarray, the generated high-throughput data provide gene–gene interaction relationships with more opportunities based on gene expression data. Several approaches are introduced to reconstruct the GRNs, but low accuracy is a major drawback. Hence, this paper introduces the hybrid distance measure and the Pearson’s correlation coefficient for reconstructing GRN. The hybrid distance, such as Tversky index, Tanimoto similarity, and Minkowski distance, is employed to connect the edges. The asymmetric partial correlation network is introduced for determining two influence functions for every pair, and edge direction is determined among them. However, the direction of edges is unknown usually and seems difficult to be identified based on gene expression data. Thus, it extend...
International Journal for Research in Applied Science and Engineering Technology
Molecular networks involve interacting proteins, RNA, and DNA molecules, which underlie the major... more Molecular networks involve interacting proteins, RNA, and DNA molecules, which underlie the major functions of living cells. DNA microarray probes how the gene expression changes to perform complex coordinated tasks in adaptation to a changing environment at a genome-wide scale. Microarray is a technology that has been widely used to probe the presence of genes in a sample of DNA or RNA. This technology helps to check the expression levels of thousands of genes together. The DNA microarray was established as a tool for the efficient collection of mRNA expression for a large number of genes. The mapping function route maps pairs of genes that present similar positive, and negative interactions and also defines how the range of each gene is going to be segmented. From all the combinations a function transforms each pair of labels into another one that classifies the type of interaction. This project addresses the challenge of reconstructing molecular networks and gene regulation from gene expression data. Reconstruction of gene regulatory networks which can also be called reverse engineering is a process of identifying gene interaction networks from the experimental microarray gene expression profiles through computation techniques. The main features involved in the computation of interaction in the filtered genes are the discretization mapping function, gene-gene mapping function, and filtering function.
International Journal for Research in Applied Science and Engineering Technology
Computational molecular biology is the superset of Bioinformatics. Areas such as genomics, mappin... more Computational molecular biology is the superset of Bioinformatics. Areas such as genomics, mapping, sequencing, determination of sequence and structure are applied by computer and data science classically. Computational biology is the application of computational tools and techniques to molecular biology. It is a multidisciplinary area of study that combines biology, computer science and statistics. There are different types of problems in computational biology such as how to recognize genes in DNA sequences that contain regulatory information, to determine interaction and regulation of all genes, to predict the structure of proteins, to forecast the functions of newly discovered proteins, to cluster and classify proteins into families and to align similar proteins etc. computational biology driven by two factors, 1. The recent explosion in the amount of available biological data, 2. Commercial incentive to exploit the available biological data for drug discovery and other developments. The algorithms are computationally expensive and computational patterns range from regular to very irregular structured patterns. This outlines computational issues related to parallelism i.e., parallel programming approaches which involves increasing efficiency and creates accurate outcomes.
IJARCCE
A vast amount of data is generated in the fields of healthcare and diagnostics, doctors have to m... more A vast amount of data is generated in the fields of healthcare and diagnostics, doctors have to make a direct contact with patients to determine the wounds, injuries and diseases by which the patient is affected. This paper highlights the application of classifying and predicting a specific disease by implementing the operations on medical data generated in the field of medical and healthcare. The proposed system can solve difficult queries for detecting a particular disease and also can assist medical practitioners to make smart clinical decisions which traditional decision support systems were not able to. The decisions taken by medical practitioners with the help of technology can result in effective and low cost treatments. In this paper, data mining methods namely, Naive Bayes and J48 algorithms are compared for testing their accuracy and performance on the training medical datasets.
Indian Journal of Science and Technology, 2016
Objectives: The objective of the proposing system is to classify Computed Tomographyimages into t... more Objectives: The objective of the proposing system is to classify Computed Tomographyimages into two classifications like cancernodes and without cancernodes for lung pictures using SVM and PSO classifiers. Methods/Analysis: Computed tomography (CT) cancerous lung images are used. Techniques employed include the usage of support vector machine and particle swarm optimization classifiers. Proposed techniques include histogram equalization, ROI masking using thresholding, wavelet transform, gray level co-occurrence matrix. Hence resulting in results in high accuracy, also yields better result in terms of the recognition accuracy. Findings: SVM shows higher accuracy rate of identifying the cancerous nodules than PSO for different iterations held. These comparison results between SVM and PSO bring out the efficiency of comparing between the two classifications algorithms. In the existing system 1.afusion of classification techniques was used to determine the lung nodes in the CT image and feature extraction based technique was implemented on the node. 2. As the second step, the features were computed based on its texture to distinguish the blood vessels. The proposed system is implemented to classify by combining of SVM and PSO. Hence leading to Low performance and accuracy in classification. With the results obtained in the proposed system it differs in various aspects like the lung nodes are well identified, better result obtained due to comparisons hence highlights the fact that proposed system had far more advantages than the existing system.
2013 7th International Conference on Intelligent Systems and Control (ISCO), 2013
In this paper, we present a systematic and conceptual overview of methods for inferring gene regu... more In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Three different inference methods are used namely, ARACNE, CLR, and MRNET. Further, we generate the network containing the gene-gene interactions and compare the inference methods by calculating their accuracy.
International Journal of Innovative Research in Computer and Communication Engineering, 2014
The DNA microarray has been established as a tool for efficient collection of mRNA expression dat... more The DNA microarray has been established as a tool for efficient collection of mRNA expression data for a large number of genes simultaneously.Mapping function approach maps pairs of genes that present similar positive and/or negative interactions and also specifies how the range of each gene is going to be segmented (labels). From all the label combinations a function transforms each pair of labels into another one, which identifies the type of interaction.
Segmentation of human brain from MRI slices for identification of brain lesions has become one of... more Segmentation of human brain from MRI slices for identification of brain lesions has become one of the most active research areas in the field of medical image processing. Brain segmentation has various important applications in diagnosing a number of disorders. The main aim of this project work is to recognize lesions and its qualifications from a specific MRI scan of a brain image and compute the area of each lesion by thresholding. Thresholding approach segment scalar images by generating a binary partitioning of the image intensities. Otsu’s technique is used to automatically carry out intensity based image thresholding. The quantitative analysis of MRI brain lesions allows obtaining useful key information about the lesions. Segmentation is done on basis of threshold, due to which whole image is converted into binary image.
Gene regulation refers to a number of sequential processes, the most well-known and understood be... more Gene regulation refers to a number of sequential processes, the most well-known and understood being translation and transcription, which control the level of a gene’s expression and ultimately result with specific quantity of a target protein. Reconstruction of gene regulatory networks is a process of analyzing the steps involved in gene regulation using computational techniques. In this paper, cancer-specific gene regulatory network has been reconstructed using information theoretic approach-Mutual Information. The microarray database used contains 12 Gene samples each of breast cancer and prostate cancer having both normal and tumor cell information. This data has been preprocessed, normalized and filtered using the t-test; the MI value is applied on the filtered genes to determine the Gene-Gene Interaction. Based on the interactions obtained, 10 different networks have been constructed and the statistical analysis has been performed on that network. Finally, validation of the in...
ABSTRACT: Gene regulation refers to a number of sequential processes, the most well-known and und... more ABSTRACT: Gene regulation refers to a number of sequential processes, the most well-known and understood being translation and transcription, which control the level of a gene's expression and ultimately result with specific quantity of a target protein. Reconstruction of gene regulatory networks is a process of analyzing the steps involved in gene regulation using computational techniques. In this paper, cancer-specific gene regulatory network has been reconstructed using information theoretic approach-Mutual Information. The microarray database used contains 12 Gene samples each of breast cancer and prostate cancer having both normal and tumor cell information. This data has been preprocessed, normalized and filtered using the t-test; the MI value is applied on the filtered genes to determine the Gene-Gene Interaction. Based on the interactions obtained, 10 different networks have been constructed and the statistical analysis has been performed on that network. Finally, valid...
Computational molecular biology is the superset of Bioinformatics. Areas such as genomics, mappin... more Computational molecular biology is the superset of Bioinformatics. Areas such as genomics, mapping, sequencing, determination of sequence and structure are applied by computer and data science classically. Computational biology is the application of computational tools and techniques to molecular biology. It is a multidisciplinary area of study that combines biology, computer science and statistics. There are different types of problems in computational biology such as how to recognize genes in DNA sequences that contain regulatory information, to determine interaction and regulation of all genes, to predict the structure of proteins, to forecast the functions of newly discovered proteins, to cluster and classify proteins into families and to align similar proteins etc. computational biology driven by two factors, 1. The recent explosion in the amount of available biological data, 2. Commercial incentive to exploit the available biological data for drug discovery and other developments. The algorithms are computationally expensive and computational patterns range from regular to very irregular structured patterns. This outlines computational issues related to parallelism i.e., parallel programming approaches which involves increasing efficiency and creates accurate outcomes.