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Nature Inspired Algorithm for Biclustering Microarray Data Analysis
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Bester Preis: Fr. 14.67 (€ 14.99)¹ (vom 01.04.2018)A Nature Inspired Algorithm for Biclustering Microarray Data Analysis (2015)
ISBN: 9783668619524 bzw. 3668619522, vermutlich in Englisch, GRIN Publishing, neu, E-Book, elektronischer Download.
A Nature Inspired Algorithm for Biclustering Microarray Data Analysis: 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. Englisch, Ebook.
Biclustering of Expression Data with Heuristic Approach (eBook, PDF) (2015)
ISBN: 9783668619524 bzw. 3668619522, in Deutsch, GRIN Publishing, neu, E-Book.
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 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. Sofort per Download lieferbar Lieferzeit 1-2 Werktage.
A Nature Inspired Algorithm for Biclustering Microarray Data Analysis (2015)
ISBN: 9783668619531 bzw. 3668619530, in Deutsch, Grin Verlag, gebundenes Buch, neu.
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 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. Versandfertig in 3-5 Tagen Lieferzeit 1-2 Werktage.
A Nature Inspired Algorithm for Biclustering Microarray Data Analysis (2017)
ISBN: 9783668619531 bzw. 3668619530, in Deutsch, GRIN Verlag, Taschenbuch, neu, Nachdruck.
PRINT ON DEMAND Book; New; Publication Year 2017; Not Signed; Fast Shipping from the UK.
Nature Inspired Algorithm for Biclustering Microarray Data Analysis
ISBN: 9783668619524 bzw. 3668619522, vermutlich in Englisch, A Nature Inspired Algorithm for Biclustering Microarray Data Analysis, neu, E-Book, elektronischer Download.
Nature Inspired Algorithm for Biclustering M (2018)
ISBN: 9783668619531 bzw. 3668619530, vermutlich in Englisch, Taschenbuch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Nature Inspired Algorithm for Biclustering Microarray Data Analysis
ISBN: 3668619530 bzw. 9783668619531, in Deutsch, GRIN Verlag, Taschenbuch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Nature Inspired Algorithm for Biclustering Microarray Data Analysis
ISBN: 3668619530 bzw. 9783668619531, vermutlich in Englisch, GRIN Verlag, Taschenbuch, neu.
Biclustering of Expression Data with Heuristic Approach als eBook von B. Rengeswaran, A.M. Natarajan, K. Premalatha
ISBN: 9783668619524 bzw. 3668619522, in Deutsch, GRIN Publishing, neu, E-Book.
Biclustering of Expression Data with Heuristic Approach ab 12.99 EURO.
Nature Inspired Algorithm for Biclustering Microarray Data Analysis (2018)
ISBN: 9783668619531 bzw. 3668619530, in Englisch, 48 Seiten, Grin Verlag, Taschenbuch, neu.
Von Händler/Antiquariat, LitoA Buch- und Medienhandel.
Taschenbuch, Label: Grin Verlag, Grin Verlag, Produktgruppe: Book, Publiziert: 2018-03-19, Studio: Grin Verlag.