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Three clustering algorithms and their application to microarray data: A Study of Three clustering algorithms and their application to microarray data - Tapa blanda
Sinopsis
In the context of medical diagnostics, an important problem is to find the genes that are correlated with given phenotypes. These genes may reveal insights to biological processes and may be used to predict the phenotypes associated to samples of RNA. To that end, two new clustering methods are presented and studied. Our first algorithm allows us to analyze cell evolution by observing how the state of every gene changes over time. Our second algorithm cluster genes whose expression profiles are similar by using a classification of the samples utilized in the microarray experiments. This classification is based upon one or more conditions that affect the composition of the samples analyzed. By using the label of the microarray experiments,extra information is provided to cluster genes. The research reported here on the first two algorithms presented consists of three parts: 1. testing our methods on artificial datasets sampled from the probabilistic models on which our methods are based, 2. using our methods on microarray expression datasets to cluster genes, 3. and comparing results from parts 1 and 2 with the results obtained by other clustering methods on the same datasets.
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In the context of medical diagnostics, an important problem is to find the genes that are correlated with given phenotypes. These genes may reveal insights to biological processes and may be used to predict the phenotypes associated to samples of RNA. To that end, two new clustering methods are presented and studied. Our first algorithm allows us to analyze cell evolution by observing how the state of every gene changes over time. Our second algorithm cluster genes whose expression profiles are similar by using a classification of the samples utilized in the microarray experiments. This classification is based upon one or more conditions that affect the composition of the samples analyzed. By using the label of the microarray experiments,extra information is provided to cluster genes. The research reported here on the first two algorithms presented consists of three parts: 1. testing our methods on artificial datasets sampled from the probabilistic models on which our methods are based, 2. using our methods on microarray expression datasets to cluster genes, 3. and comparing results from parts 1 and 2 with the results obtained by other clustering methods on the same datasets.
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Three clustering algorithms and their application to microarray data
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LAP LAMBERT Academic Publishing Feb 2010, 2010
ISBN 10: 3838347943
ISBN 13: 9783838347943
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the context of medical diagnostics, an important problem is to find the genes that are correlated with given phenotypes. These genes may reveal insights to biological processes and may be used to predict the phenotypes associated to samples of RNA. To that end, two new clustering methods are presented and studied. Our first algorithm allows us to analyze cell evolution by observing how the state of every gene changes over time. Our second algorithm cluster genes whose expression profiles are similar by using a classification of the samples utilized in the microarray experiments. This classification is based upon one or more conditions that affect the composition of the samples analyzed. By using the label of the microarray experiments,extra information is provided to cluster genes. The research reported here on the first two algorithms presented consists of three parts: 1. testing our methods on artificial datasets sampled from the probabilistic models on which our methods are based, 2. using our methods on microarray expression datasets to cluster genes, 3. and comparing results from parts 1 and 2 with the results obtained by other clustering methods on the same datasets. 96 pp. Englisch. Nº de ref. del artículo: 9783838347943
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Three clustering algorithms and their application to microarray data
Publicado por
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838347943
ISBN 13: 9783838347943
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Molina Lopez Francisco JavierFrancisco Javier Molina Lopez is a State Statistician at DGT.He has a Ph.D. of the Department of Mathematics-University of California: UCSC-UC Berkeley. He worked in UCSC as a teacher assistant during ten. Nº de ref. del artículo: 5415225
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Three clustering algorithms and their application to microarray data : A Study of Three clustering algorithms and their application to microarray data
Publicado por
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838347943
ISBN 13: 9783838347943
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the context of medical diagnostics, an important problem is to find the genes that are correlated with given phenotypes. These genes may reveal insights to biological processes and may be used to predict the phenotypes associated to samples of RNA. To that end, two new clustering methods are presented and studied. Our first algorithm allows us to analyze cell evolution by observing how the state of every gene changes over time. Our second algorithm cluster genes whose expression profiles are similar by using a classification of the samples utilized in the microarray experiments. This classification is based upon one or more conditions that affect the composition of the samples analyzed. By using the label of the microarray experiments,extra information is provided to cluster genes. The research reported here on the first two algorithms presented consists of three parts: 1. testing our methods on artificial datasets sampled from the probabilistic models on which our methods are based, 2. using our methods on microarray expression datasets to cluster genes, 3. and comparing results from parts 1 and 2 with the results obtained by other clustering methods on the same datasets. Nº de ref. del artículo: 9783838347943
Cantidad disponible: 1 disponibles
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Three clustering algorithms and their application to microarray data
Publicado por
LAP LAMBERT Academic Publishing Feb 2010, 2010
ISBN 10: 3838347943
ISBN 13: 9783838347943
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -In the context of medical diagnostics, an important problem is to find the genes that are correlated with given phenotypes. These genes may reveal insights to biological processes and may be used to predict the phenotypes associated to samples of RNA. To that end, two new clustering methods are presented and studied. Our first algorithm allows us to analyze cell evolution by observing how the state of every gene changes over time. Our second algorithm cluster genes whose expression profiles are similar by using a classification of the samples utilized in the microarray experiments. This classification is based upon one or more conditions that affect the composition of the samples analyzed. By using the label of the microarray experiments,extra information is provided to cluster genes. The research reported here on the first two algorithms presented consists of three parts: 1. testing our methods on artificial datasets sampled from the probabilistic models on which our methods are based, 2. using our methods on microarray expression datasets to cluster genes, 3. and comparing results from parts 1 and 2 with the results obtained by other clustering methods on the same datasets.Books on Demand GmbH, Überseering 33, 22297 Hamburg 96 pp. Englisch. Nº de ref. del artículo: 9783838347943
Cantidad disponible: 2 disponibles