Isbn: 9786134923200 - ubiquitination ptm sites prediction via random forest algorithm (6 Ergebnisse)

Paperback. Zustand: Brand New. 140 pages. 8.66x5.91x0.32 inches. In Stock.

Taschenbuch. Zustand: Neu. Ubiquitination PTM Sites Prediction via Random Forest Algorithm | Shakil Ahmed (u. a.) | Taschenbuch | 140 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786134923200 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Proteomics is the modern research window in Bioinformatics for drug discovery and personalized medicine. The post-translation modification (PTM) site prediction is one of the top significant research wings in the Proteomics. There are more than 200 PTM sites in the literature, and the ubiquitination is one of them. It can involve in lots of biological processes and closely implicated with various diseases. The identification of ubiquitination site is an important task for understanding the mechanisms of disease due to ubiquitination. However, the identification of ubiquitination sites in experimental approaches is time consuming and costly. As an alternative, computational identification is more useful and reliable. The random forest (RF) algorithm used with some encoding schemes for feature selection and to develop a predictor for identification of ubiquitination PTM sites. Random forest is the efficient statistical machine learning tool for multivariate classification and regression. The RF based method achieves significantly better performances for prediction of protein ubiquitination PTM sites. 140 pp. Englisch.…

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Proteomics is the modern research window in Bioinformatics for drug discovery and personalized medicine. The post-translation modification (PTM) site prediction is one of the top significant research wings in the Proteomics. There are more than 200 PTM sites in the literature, and the ubiquitination is one of them. It can involve in lots of biological processes and closely implicated with various diseases. The identification of ubiquitination site is an important task for understanding the mechanisms of disease due to ubiquitination. However, the identification of ubiquitination sites in experimental approaches is time consuming and costly. As an alternative, computational identification is more useful and reliable. The random forest (RF) algorithm used with some encoding schemes for feature selection and to develop a predictor for identification of ubiquitination PTM sites. Random forest is the efficient statistical machine learning tool for multivariate classification and regression. The RF based method achieves significantly better performances for prediction of protein ubiquitination PTM sites.…

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: AHMED SHAKILMd. Shakil Ahmed is a Researcher and Fiction Writer. He Received his B.Sc and M.Sc in Statistics and Bioinformatics from the University of Rajshahi, Bangladesh. He has published 6 articles in the international journals wi. …

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Proteomics is the modern research window in Bioinformatics for drug discovery and personalized medicine. The post-translation modification (PTM) site prediction is one of the top significant research wings in the Proteomics. There are more than 200 PTM sites in the literature, and the ubiquitination is one of them. It can involve in lots of biological processes and closely implicated with various diseases. The identification of ubiquitination site is an important task for understanding the mechanisms of disease due to ubiquitination. However, the identification of ubiquitination sites in experimental approaches is time consuming and costly. As an alternative, computational identification is more useful and reliable. The random forest (RF) algorithm used with some encoding schemes for feature selection and to develop a predictor for identification of ubiquitination PTM sites. Random forest is the efficient statistical machine learning tool for multivariate classification and regression. The RF based method achieves significantly better performances for prediction of protein ubiquitination PTM sites.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch.…