9786138965282 - Data Analytics in Gene Expression Profiling: Exploring gene expression for cancer di Kabir Ahmad, Farzana (8 risultati)

Condizione: New.

Taschenbuch. Condizione: Neu. Data Analytics in Gene Expression Profiling | Exploring gene expression for cancer | Farzana Kabir Ahmad | Taschenbuch | Englisch | 2021 | Scholars' Press | EAN 9786138965282 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Microarray technology has shifted to a new era in molecular classification, however, interpreting gene expression data to remain a challenging issue due to their innate nature of 'high dimensional low sample size'. Furthermore, this data is often overwhelmed, overfitting and confused by the complexity of data analysis. Small sample size and a large number of variables to be analysed posed significant challenges during data analysis, mainly in learning network structure. Moreover, the ability to study the gene interactions that form tumour growth is a great difficulty to computational biology researchers as gene does not work alone but involves complex interactions. This book aims to propose a dynamic Bayesian network-based model in order to identify gene signatures from large-scale gene expression profiles. The dynamic Bayesian network-based model attempts to discover the gene regulation that yields to breast cancer progression. 112 pp. Englisch.

Condizione: New. Print on Demand.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kabir Ahmad FarzanaDr. Farzana Kabir Ahmad is a senior lecturer at the School of Computing, Universiti Utara Malaysia, MALAYSIA. She pursued her Ph.D. in Computer Science (Bioinformatics) from Universiti Teknologi Malaysia in 20.

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Microarray technology has shifted to a new era in molecular classification, however, interpreting gene expression data to remain a challenging issue due to their innate nature of 'high dimensional low sample size'. Furthermore, this data is often overwhelmed, overfitting and confused by the complexity of data analysis. Small sample size and a large number of variables to be analysed posed significant challenges during data analysis, mainly in learning network structure. Moreover, the ability to study the gene interactions that form tumour growth is a great difficulty to computational biology researchers as gene does not work alone but involves complex interactions. This book aims to propose a dynamic Bayesian network-based model in order to identify gene signatures from large-scale gene expression profiles. The dynamic Bayesian network-based model attempts to discover the gene regulation that yields to breast cancer progression.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 112 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Microarray technology has shifted to a new era in molecular classification, however, interpreting gene expression data to remain a challenging issue due to their innate nature of 'high dimensional low sample size'. Furthermore, this data is often overwhelmed, overfitting and confused by the complexity of data analysis. Small sample size and a large number of variables to be analysed posed significant challenges during data analysis, mainly in learning network structure. Moreover, the ability to study the gene interactions that form tumour growth is a great difficulty to computational biology researchers as gene does not work alone but involves complex interactions. This book aims to propose a dynamic Bayesian network-based model in order to identify gene signatures from large-scale gene expression profiles. The dynamic Bayesian network-based model attempts to discover the gene regulation that yields to breast cancer progression.