Balamurugan R

Bio: Balamurugan R is an academic researcher from Bannari Amman Institute of Technology, Sathy. The author has contributed to research in topics: Workflow & Cluster analysis. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Cancer Diagnosis Epigenomics Scientific Workflow Scheduling in the Cloud Computing Environment Using an Improved PSO Algorithm

Abstract: Objective: Epigenetic modifications involving DNA methylation and histone statud are responsible for the stable maintenance of cellular phenotypes. Abnormalities may be causally involved in cancer development and therefore could have diagnostic potential. The field of epigenomics refers to all epigenetic modifications implicated in control of gene expression, with a focus on better understanding of human biology in both normal and pathological states. Epigenomics scientific workflow is essentially a data processing pipeline to automate the execution of various genome sequencing operations or tasks. Cloud platform is a popular computing platform for deploying large scale epigenomics scientific workflow. Its dynamic environment provides various resources to scientific users on a pay-per-use billing model. Scheduling epigenomics scientific workflow tasks is a complicated problem in cloud platform. We here focused on application of an improved particle swam optimization (IPSO) algorithm for this purpose. Methods: The IPSO algorithm was applied to find suitable resources and allocate epigenomics tasks so that the total cost was minimized for detection of epigenetic abnormalities of potential application for cancer diagnosis. Result: The results showed that IPSO based task to resource mapping reduced total cost by 6.83 percent as compared to the traditional PSO algorithm. Conclusion: The results for various cancer diagnosis tasks showed that IPSO based task to resource mapping can achieve better costs when compared to PSO based mapping for epigenomics scientific application workflow.

Cancer Detection in Microarray Data Using a Modified Cat Swarm Optimization Clustering Approach

Abstract: Objective: A better understanding of functional genomics can be obtained by extracting patterns hidden in gene expression data. This could have paramount implications for cancer diagnosis, gene treatments and other domains. Clustering may reveal natural structures and identify interesting patterns in underlying data. The main objective of this research was to derive a heuristic approach to detection of highly co-expressed genes related to cancer from gene expression data with minimum Mean Squared Error (MSE). Methods: A modified CSO algorithm using Harmony Search (MCSO-HS) for clustering cancer gene expression data was applied. Experiment results are analyzed using two cancer gene expression benchmark datasets, namely for leukaemia and for breast cancer. Result: The results indicated MCSO-HS to be better than HS and CSO, 13% and 9% with the leukaemia dataset. For breast cancer dataset improvement was by 22% and 17%, respectively, in terms of MSE. Conclusion: The results showed MCSO-HS to outperform HS and CSO with both benchmark datasets. To validate the clustering results, this work was tested with internal and external cluster validation indices. Also this work points to biological validation of clusters with gene ontology in terms of function, process and component.

Clustering cancer gene expression data by projective clustering ensemble

Abstract: Gene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus various projective clustering techniques and ensemble techniques have been suggested to combat with these challenges. However, it is rather challenging to synergy these two kinds of techniques together to avoid the curse of dimensionality problem and to boost the performance of gene expression data clustering. In this paper, we employ a projective clustering ensemble (PCE) to integrate the advantages of projective clustering and ensemble clustering, and to avoid the dilemma of combining multiple projective clusterings. Our experimental results on publicly available cancer gene expression data show PCE can improve the quality of clustering gene expression data by at least 4.5% (on average) than other related techniques, including dimensionality reduction based single clustering and ensemble approaches. The empirical study demonstrates that, to further boost the performance of clustering cancer gene expression data, it is necessary and promising to synergy projective clustering with ensemble clustering. PCE can serve as an effective alternative technique for clustering gene expression data.

Hybrid Genetic Algorithm and Simulated Annealing for Clustering Microarray Gene Expression data

Abstract: Gene expression is the process by which information in gene is used to create proteins. The gene expression studies generate large amount of data. These data, referred to as the gene expression matrix, represent the expression levels for thousands of genes recorded at a few time instances. A typical microarray experiment involves the hybridization of an mRNA molecule to the DNA template from which it is originated. Many DNA samples are used to construct an array. The amount of mRNA bound to each site on the array indicates the expression level of the various genes. This number may run in thousands. All the data is collected and a profile is generated for gene expression in the cell. Clustering is a process of partitioning a set of meaningful subclasses called clusters. Clustering is a key step in the analysis of gene expression data. Genetic Algorithms are a family of computational models inspired by evolution. The searching capability of genetic algorithms is exploited in order to search for appropriate cluster center in feature space such that a similarity metric of resulting clusters is optimized. The chromosome which are represented as strings of real numbers, encode the centers of fixed number of clusters. The experiment results are demonstrated on real data sets and the performance of GA is evaluated in comparison with the state-of-the art algorithm K-Means with use of internal validation criteria.

The roles of cloud-based systems on the cancer-related studies: a systematic literature review

Abstract: The advances in Wireless-based technologies and intelligent diagnostics and forecasting such as cloud computing have significantly affected our lifestyle, observed in many fields, especially healthcare. Also, since the number of new cases of cancer has become very high, there is a need to investigate this matter deeply. Still, there is no systematic review on the application or implementation of the cloud in cancer-care services. Hence, this paper has introduced a comprehensive review of a cloud-centered healthcare system that emphasizes treatment ways in different types of cancer until Sep 2021. The results have shown that the largest study was about the relationship between cancer and the cloud associated with breast cancer. Also, the results have shown that cloud computing facilitates data protection, privacy, and medical record access. Using cloud computing in hospitals, physicians will use advanced programs and tools, and nurses will quickly access patients’ information with new Wireless-based technologies. A strong understanding of the practical aspects of cloud computing will help researchers effectively navigate the vast data ecosystems in cancer research. So, by highlighting the advantages and drawbacks of analyzed articles, this study provides a comprehensive and up-to-date report on the field of cloud-based cancer studies to fill the previous gaps.