Prediction of Ubiquitination Sites Using UbiNets
Joint Authors
Yadav, Sarthak
Gupta, Manoj
Bist, Ankur Singh
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-20
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Ubiquitination controls the activity of various proteins and belongs to posttranslational modification.
Various machine learning techniques are taken for prediction of ubiquitination sites in protein sequences.
The paper proposes a new MLP architecture, named UbiNets, which is based on Densely Connected Convolutional Neural Networks (DenseNet).
Computational machine learning techniques, such as Random Forest Classifier, Gradient Boosting Machines, and Multilayer Perceptrons (MLP), are taken for analysis.
The main target of this paper is to explore the significance of deep learning techniques for the prediction of ubiquitination sites in protein sequences.
Furthermore, the results obtained show that the newly proposed model provides significant accuracy.
Satisfactory experimental results show the efficiency of proposed method for the prediction of ubiquitination sites in protein sequences.
Further, it has been recommended that this method can be used to sort out real time problems in concerned domain.
American Psychological Association (APA)
Yadav, Sarthak& Gupta, Manoj& Bist, Ankur Singh. 2018. Prediction of Ubiquitination Sites Using UbiNets. Advances in Fuzzy Systems،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-986265
Modern Language Association (MLA)
Yadav, Sarthak…[et al.]. Prediction of Ubiquitination Sites Using UbiNets. Advances in Fuzzy Systems No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-986265
American Medical Association (AMA)
Yadav, Sarthak& Gupta, Manoj& Bist, Ankur Singh. Prediction of Ubiquitination Sites Using UbiNets. Advances in Fuzzy Systems. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-986265
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-986265