A Novel Deep Learning Approach for the Prediction of Arabidopsis Thaliana Ubiquitination Sites

Proceedings of the 13th International Conference on Information Technology and Its Applications (CITA 2024); pp: 48-57.

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Những tác giả chính:	Tran, Thi Xuan, Nguyen, Thi Tuyen, Le, Nguyen Quoc Khanh, Nguyen, Hong Hai, Nguyen, Van Nui
格式:	Bài viết
語言:	English
出版:	Vietnam-Korea University of Information and Communication Technology 2024
主題:	Ubiquitilation A. thaliana CNN LSTM Deep learning Natural language processing
在線閱讀:	https://elib.vku.udn.vn/handle/123456789/4010
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

Thư viện lưu trữ:	Trường Đại học Công nghệ Thông tin và Truyền thông Việt Hàn - Đại học Đà Nẵng

id	oai:elib.vku.udn.vn:123456789-4010
record_format	dspace
spelling	oai:elib.vku.udn.vn:123456789-40102025-07-01T09:45:28Z A Novel Deep Learning Approach for the Prediction of Arabidopsis Thaliana Ubiquitination Sites Tran, Thi Xuan Nguyen, Thi Tuyen Le, Nguyen Quoc Khanh Nguyen, Hong Hai Nguyen, Van Nui Ubiquitilation A. thaliana CNN LSTM Deep learning Natural language processing Proceedings of the 13th International Conference on Information Technology and Its Applications (CITA 2024); pp: 48-57. Protein ubiquitination is a crucial post-translational modification involving the attachment of ubiquitin molecules to proteins, forming ubiquitin-protein complexes. This modification plays a pivotal role in various biological processes, including protein decomposition, regulation of enzymatic activity, modulation of interactions, cell cycle regulation, and the onset of serious diseases such as cancer, diabetes, Parkinson's, Alzheimer's, and cardiovascular diseases. Scientists have devoted extensive research to developing tools for predicting ubiquitination in different species. These tools primarily rely on predefined sequence features and machine learning algorithms. However, the variations in the ubiquitination cascade among species remain poorly understood. While machine learning algorithms typically focus on the physical and chemical characteristics of previously analyzed proteins, deep learning algorithms can automatically extract features from protein language strings. Nevertheless, there are currently limited studies that simultaneously incorporate both types of features. In this study, we present a novel approach for predicting ubiquitination sites in Arabidopsis thaliana. We build a deep learning-based combined model that integrates both the chemical and physical characteristics of proteins and other natural language features of proteins. Our results demonstrate that our proposed model outperforms previous machine learning algorithms and prediction tools for A. thaliana ubiquitination sites. We anticipate that these findings will prove valuable to researchers in their respective studies. 2024-07-30T08:39:24Z 2024-07-30T08:39:24Z 2024-07 Working Paper 978-604-80-9774-5 https://elib.vku.udn.vn/handle/123456789/4010 en CITA; application/pdf Vietnam-Korea University of Information and Communication Technology
institution	Trường Đại học Công nghệ Thông tin và Truyền thông Việt Hàn - Đại học Đà Nẵng
collection	DSpace
language	English
topic	Ubiquitilation A. thaliana CNN LSTM Deep learning Natural language processing
spellingShingle	Ubiquitilation A. thaliana CNN LSTM Deep learning Natural language processing Tran, Thi Xuan Nguyen, Thi Tuyen Le, Nguyen Quoc Khanh Nguyen, Hong Hai Nguyen, Van Nui A Novel Deep Learning Approach for the Prediction of Arabidopsis Thaliana Ubiquitination Sites
description	Proceedings of the 13th International Conference on Information Technology and Its Applications (CITA 2024); pp: 48-57.
format	Working Paper
author	Tran, Thi Xuan Nguyen, Thi Tuyen Le, Nguyen Quoc Khanh Nguyen, Hong Hai Nguyen, Van Nui
author_facet	Tran, Thi Xuan Nguyen, Thi Tuyen Le, Nguyen Quoc Khanh Nguyen, Hong Hai Nguyen, Van Nui
author_sort	Tran, Thi Xuan
title	A Novel Deep Learning Approach for the Prediction of Arabidopsis Thaliana Ubiquitination Sites
title_short	A Novel Deep Learning Approach for the Prediction of Arabidopsis Thaliana Ubiquitination Sites
title_full	A Novel Deep Learning Approach for the Prediction of Arabidopsis Thaliana Ubiquitination Sites
title_fullStr	A Novel Deep Learning Approach for the Prediction of Arabidopsis Thaliana Ubiquitination Sites
title_full_unstemmed	A Novel Deep Learning Approach for the Prediction of Arabidopsis Thaliana Ubiquitination Sites
title_sort	novel deep learning approach for the prediction of arabidopsis thaliana ubiquitination sites
publisher	Vietnam-Korea University of Information and Communication Technology
publishDate	2024
url	https://elib.vku.udn.vn/handle/123456789/4010
_version_	1849199992073355264

A Novel Deep Learning Approach for the Prediction of Arabidopsis Thaliana Ubiquitination Sites

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