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Sinopsis
Research Paper (undergraduate) from the year 2015 in the subject Computer Science - Bioinformatics, grade: 1, Bannari Amman Institute of Technology, language: English, abstract: Extracting meaningful information from gene expression data poses a great challenge to the community of researchers in the field of computation as well as to biologists. It is possible to determine the behavioral patterns of genes such as nature of their interaction, similarity of their behavior and so on, through the analysis of gene expression data. If two different genes show similar expression patterns across the samples, this suggests a common pattern of regulation or relationship between their functions. These patterns have huge significance and application in bioinformatics and clinical research such as drug discovery, treatment planning, accurate diagnosis, prognosis, protein network analysis and so on. In order to identify various patterns from gene expression data, data mining techniques are essential. Major data mining techniques which can be applied for the analysis of gene expression data include clustering, classification, association rule mining etc. Clustering is an important data mining technique for the analysis of gene expression data. However clustering has some disadvantages. To overcome the problems associated with clustering, biclustering is introduced. Clustering is a global model where as biclustering is a local model. Discovering such local expression patterns is essential for identifying many genetic pathways that are not apparent otherwise. It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data. Biclustering is a two dimensional clustering problem where we group the genes and samples simultaneously. It has a great potential in detecting marker genes that are associated with certain tissues or diseases. However, since the problem is NP-hard, there has b
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Acerca del autor
Balamurugan Rengeswaran received the B.E. degree in Computer Science and Engineering in 2010 from the Government College of Engineering, Salem and the M.E. degree in Computer Science and Engineering in 2012 from the Bannari Amman Institute of Technology, Sathyamangalam. He is completed his Ph.D in Information and Communication Engineering in 2016 from Anna University, Chennai. Currently he is working as an Associate Professor in Department of Computer Science and Engineering in Bharat Institute of Engineering and Technology, Hyderabad. He has published more than 17 papers in various international journals and conferences. His areas of interest include data mining and meta-heuristic optimization techniques.
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- EditorialGRIN Verlag
- Año de publicación2018
- ISBN 10 3668619530
- ISBN 13 9783668619531
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de edición1
- Número de páginas48
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A Nature Inspired Algorithm for Biclustering Microarray Data Analysis
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ISBN 10: 3668619530
ISBN 13: 9783668619531
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Research Paper (undergraduate) from the year 2015 in the subject Computer Science - Bioinformatics, grade: 1, Bannari Amman Institute of Technology, language: English, abstract: Extracting meaningful information from gene expression data poses a great challenge to the community of researchers in the field of computation as well as to biologists. It is possible to determine the behavioral patterns of genes such as nature of their interaction, similarity of their behavior and so on, through the analysis of gene expression data. If two different genes show similar expression patterns across the samples, this suggests a common pattern of regulation or relationship between their functions. These patterns have huge significance and application in bioinformatics and clinical research such as drug discovery, treatment planning, accurate diagnosis, prognosis, protein network analysis and so on.In order to identify various patterns from gene expression data, data mining techniques are essential. Major data mining techniques which can be applied for the analysis of gene expression data include clustering, classification, association rule mining etc. Clustering is an important data mining technique for the analysis of gene expression data. However clustering has some disadvantages. To overcome the problems associated with clustering, biclustering is introduced. Clustering is a global model where as biclustering is a local model. Discovering such local expression patterns is essential for identifying many genetic pathways that are not apparent otherwise. It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.Biclustering is a two dimensional clustering problem where we group the genes and samples simultaneously. It has a great potential in detecting marker genes that are associated with certain tissues or diseases. However, since the problem is NP-hard, there has been a lot of research in biclustering involving statistical and graph-theoretic. The proposed Cuckoo Search (CS) method finds the significant biclusters in large expression data. The experiment results are demonstrated on benchmark datasets. Also, this work determines the biological relevance of the biclusters with Gene Ontology in terms of function. 48 pp. Englisch. Nº de ref. del artículo: 9783668619531
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A Nature Inspired Algorithm for Biclustering Microarray Data Analysis
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Research Paper (undergraduate) from the year 2015 in the subject Computer Science - Bioinformatics, grade: 1, Bannari Amman Institute of Technology, language: English, abstract: Extracting meaningful information from gene expression data poses a great challenge to the community of researchers in the field of computation as well as to biologists. It is possible to determine the behavioral patterns of genes such as nature of their interaction, similarity of their behavior and so on, through the analysis of gene expression data. If two different genes show similar expression patterns across the samples, this suggests a common pattern of regulation or relationship between their functions. These patterns have huge significance and application in bioinformatics and clinical research such as drug discovery, treatment planning, accurate diagnosis, prognosis, protein network analysis and so on.In order to identify various patterns from gene expression data, data mining techniques are essential. Major data mining techniques which can be applied for the analysis of gene expression data include clustering, classification, association rule mining etc. Clustering is an important data mining technique for the analysis of gene expression data. However clustering has some disadvantages. To overcome the problems associated with clustering, biclustering is introduced. Clustering is a global model where as biclustering is a local model. Discovering such local expression patterns is essential for identifying many genetic pathways that are not apparent otherwise. It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.Biclustering is a two dimensional clustering problem where we group the genes and samples simultaneously. It has a great potential in detecting marker genes that are associated with certain tissues or diseases. However, since the problem is NP-hard, there has been a lot of research in biclustering involving statistical and graph-theoretic. The proposed Cuckoo Search (CS) method finds the significant biclusters in large expression data. The experiment results are demonstrated on benchmark datasets. Also, this work determines the biological relevance of the biclusters with Gene Ontology in terms of function. Nº de ref. del artículo: 9783668619531
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A Nature Inspired Algorithm for Biclustering Microarray Data Analysis
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ISBN 10: 3668619530
ISBN 13: 9783668619531
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Taschenbuch. Condición: Neu. Neuware -Research Paper (undergraduate) from the year 2015 in the subject Computer Science - Bioinformatics, grade: 1, Bannari Amman Institute of Technology, language: English, abstract: Extracting meaningful information from gene expression data poses a great challenge to the community of researchers in the field of computation as well as to biologists. It is possible to determine the behavioral patterns of genes such as nature of their interaction, similarity of their behavior and so on, through the analysis of gene expression data. If two different genes show similar expression patterns across the samples, this suggests a common pattern of regulation or relationship between their functions. These patterns have huge significance and application in bioinformatics and clinical research such as drug discovery, treatment planning, accurate diagnosis, prognosis, protein network analysis and so on.In order to identify various patterns from gene expression data, data mining techniques are essential. Major data mining techniques which can be applied for the analysis of gene expression data include clustering, classification, association rule mining etc. Clustering is an important data mining technique for the analysis of gene expression data. However clustering has some disadvantages. To overcome the problems associated with clustering, biclustering is introduced. Clustering is a global model where as biclustering is a local model. Discovering such local expression patterns is essential for identifying many genetic pathways that are not apparent otherwise. It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.Biclustering is a two dimensional clustering problem where we group the genes and samples simultaneously. It has a great potential in detecting marker genes that are associated with certain tissues or diseases. However, since the problem is NP-hard, there has been a lot of research in biclustering involving statistical and graph-theoretic. The proposed Cuckoo Search (CS) method finds the significant biclusters in large expression data. The experiment results are demonstrated on benchmark datasets. Also, this work determines the biological relevance of the biclusters with Gene Ontology in terms of function.Books on Demand GmbH, Überseering 33, 22297 Hamburg 48 pp. Englisch. Nº de ref. del artículo: 9783668619531
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A Nature Inspired Algorithm for Biclustering Microarray Data Analysis
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A Nature Inspired Algorithm for Biclustering Microarray Data Analysis
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Taschenbuch. Condición: Neu. A Nature Inspired Algorithm for Biclustering Microarray Data Analysis | B. Rengeswaran (u. a.) | Taschenbuch | Paperback | 48 S. | Englisch | 2018 | GRIN Verlag | EAN 9783668619531 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Nº de ref. del artículo: 112768262
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