DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning

Urine excretory proteins are among the most commonly used biomarkers in body fluids. Computational identification of urine excretory proteins can provide very useful information for identifying targeted disease biomarkers in urine by linking transcriptome or proteomics data. There are few methods ba...

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Autores principales:	Wei Du, Ran Pang, Gaoyang Li, Huansheng Cao, Ying Li, Yanchun Liang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Bioinformatics biomedical computing machine learning data mining Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e5984f376703409582798c6bd656e1a0
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spelling	oai:doaj.org-article:e5984f376703409582798c6bd656e1a02021-11-19T00:04:21ZDeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning2169-353610.1109/ACCESS.2020.2997937https://doaj.org/article/e5984f376703409582798c6bd656e1a02020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9102239/https://doaj.org/toc/2169-3536Urine excretory proteins are among the most commonly used biomarkers in body fluids. Computational identification of urine excretory proteins can provide very useful information for identifying targeted disease biomarkers in urine by linking transcriptome or proteomics data. There are few methods based on conventional machine learning algorithms for predicting urine excretory proteins, and most of these methods strongly depend on the extraction of features from urine excretory proteins. An end-to-end model for urine excretory protein prediction, called DeepUEP, is presented using deep neural networks relying on only amino acid sequence information. The model achieves good performance and outperforms existing methods on training and testing sets. By comparing known urinary protein biomarkers with the results of the model, we find that the model can achieve a true-positive rate of over 80% for urinary protein biomarkers that have been detected in more than one study. We also combine our model with transcriptome and proteomics data from lung cancer patients to predict the potential urinary protein biomarkers of lung cancer. A web server is developed for the prediction of urine excretory proteins, and it can be accessed at the following URL: <uri>http://www.csbg-jlu.info/DeepUEP/</uri>. We believe that our prediction model and web server are useful for biomedical researchers who are interested in identifying urinary protein biomarkers, especially for candidate proteins in transcriptome or proteomics analyses of diseased tissues.Wei DuRan PangGaoyang LiHuansheng CaoYing LiYanchun LiangIEEEarticleBioinformaticsbiomedical computingmachine learningdata miningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 100251-100261 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bioinformatics biomedical computing machine learning data mining Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Bioinformatics biomedical computing machine learning data mining Electrical engineering. Electronics. Nuclear engineering TK1-9971 Wei Du Ran Pang Gaoyang Li Huansheng Cao Ying Li Yanchun Liang DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning
description	Urine excretory proteins are among the most commonly used biomarkers in body fluids. Computational identification of urine excretory proteins can provide very useful information for identifying targeted disease biomarkers in urine by linking transcriptome or proteomics data. There are few methods based on conventional machine learning algorithms for predicting urine excretory proteins, and most of these methods strongly depend on the extraction of features from urine excretory proteins. An end-to-end model for urine excretory protein prediction, called DeepUEP, is presented using deep neural networks relying on only amino acid sequence information. The model achieves good performance and outperforms existing methods on training and testing sets. By comparing known urinary protein biomarkers with the results of the model, we find that the model can achieve a true-positive rate of over 80% for urinary protein biomarkers that have been detected in more than one study. We also combine our model with transcriptome and proteomics data from lung cancer patients to predict the potential urinary protein biomarkers of lung cancer. A web server is developed for the prediction of urine excretory proteins, and it can be accessed at the following URL: <uri>http://www.csbg-jlu.info/DeepUEP/</uri>. We believe that our prediction model and web server are useful for biomedical researchers who are interested in identifying urinary protein biomarkers, especially for candidate proteins in transcriptome or proteomics analyses of diseased tissues.
format	article
author	Wei Du Ran Pang Gaoyang Li Huansheng Cao Ying Li Yanchun Liang
author_facet	Wei Du Ran Pang Gaoyang Li Huansheng Cao Ying Li Yanchun Liang
author_sort	Wei Du
title	DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning
title_short	DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning
title_full	DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning
title_fullStr	DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning
title_full_unstemmed	DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning
title_sort	deepuep: prediction of urine excretory proteins using deep learning
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/e5984f376703409582798c6bd656e1a0
work_keys_str_mv	AT weidu deepueppredictionofurineexcretoryproteinsusingdeeplearning AT ranpang deepueppredictionofurineexcretoryproteinsusingdeeplearning AT gaoyangli deepueppredictionofurineexcretoryproteinsusingdeeplearning AT huanshengcao deepueppredictionofurineexcretoryproteinsusingdeeplearning AT yingli deepueppredictionofurineexcretoryproteinsusingdeeplearning AT yanchunliang deepueppredictionofurineexcretoryproteinsusingdeeplearning
_version_	1718420701238525952

DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning

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