Ubiquitination PTM Sites Prediction via Random Forest Algorithm - Couverture souple

AHMED, SHAKIL; Reza, Selim; Kamruzzaman, .

 
9786134923200: Ubiquitination PTM Sites Prediction via Random Forest Algorithm
Couverture souple
ISBN 10 : 6134923206 ISBN 13 : 9786134923200
Editeur : LAP LAMBERT Academic Publishing, 2018

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Biographie de l'auteur

Md. 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 with good ISI impact factor and 9 full length & 20 abstract articles in the international conference proceedings.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2018
Langue
anglais
ISBN 10 
6134923206
ISBN 13 
9786134923200
Reliure
Broché
Nombre de pages
140
Coordonnées du fabricant
non disponible
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