Prediction of Ubiquitination Sites Using UbiNets
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 Conne...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2018/5125103 |
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| _version_ | 1832565040311435264 |
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| author | Sarthak Yadav Manoj Gupta Ankur Singh Bist |
| author_facet | Sarthak Yadav Manoj Gupta Ankur Singh Bist |
| author_sort | Sarthak Yadav |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-82393efd03254b37b82ed8d7072075dd |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-82393efd03254b37b82ed8d7072075dd2025-02-03T01:09:36ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2018-01-01201810.1155/2018/51251035125103Prediction of Ubiquitination Sites Using UbiNetsSarthak Yadav0Manoj Gupta1Ankur Singh Bist2Department of Computer Science and Engineering, KIET, Ghaziabad, IndiaDepartment of Computer Science and Engineering, SMVDU, Katra, Jammu and Kashmir, IndiaDepartment of Computer Science and Engineering, KIET, Ghaziabad, IndiaUbiquitination 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.http://dx.doi.org/10.1155/2018/5125103 |
| spellingShingle | Sarthak Yadav Manoj Gupta Ankur Singh Bist Prediction of Ubiquitination Sites Using UbiNets Advances in Fuzzy Systems |
| title | Prediction of Ubiquitination Sites Using UbiNets |
| title_full | Prediction of Ubiquitination Sites Using UbiNets |
| title_fullStr | Prediction of Ubiquitination Sites Using UbiNets |
| title_full_unstemmed | Prediction of Ubiquitination Sites Using UbiNets |
| title_short | Prediction of Ubiquitination Sites Using UbiNets |
| title_sort | prediction of ubiquitination sites using ubinets |
| url | http://dx.doi.org/10.1155/2018/5125103 |
| work_keys_str_mv | AT sarthakyadav predictionofubiquitinationsitesusingubinets AT manojgupta predictionofubiquitinationsitesusingubinets AT ankursinghbist predictionofubiquitinationsitesusingubinets |