PUEPro

A Computational Pipeline for Prediction of Urine Excretory Proteins

Yan Wang, Wei Du, Yanchun Liang, Xin Chen, Chi Zhang, Wei Pang, Ying Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Downloads (Pure)

Abstract

A computational pipeline is developed to accurately predict urine excretory
proteins and the possible origins of the proteins. The novel contributions
of this study include: (i) a new method for predicting if a cellular protein is
urine excretory based on unique features of proteins known to be urine excretory; and (ii) a novel method for identifying urinary proteins originating from the urinary system. By integrating these tools, our computational pipeline is capable of predicting the origin of a detected urinary protein, hence offering a novel tool for predicting potential biomarkers of a specific disease, which may have some of their proteins urine excreted. One application is presented for this prediction pipeline to demonstrate the effectiveness of its prediction. The pipeline and supplementary materials can be accessed at the following URL:http://csbl.bmb.uga.edu/PUEPro/
Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications
Subtitle of host publication2th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings
EditorsJinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng
PublisherSpringer International Publishing
Pages714-725
Number of pages12
ISBN (Electronic)978-3-319-49586-6
ISBN (Print)978-3-319-49585-9
DOIs
Publication statusPublished - 2016
EventADMA 2016 - Gold Coast, Australia
Duration: 12 Dec 201615 Dec 2016

Publication series

NameLecture Notes in Artificial Intelligence (LNAI)
PublisherSpringer

Conference

ConferenceADMA 2016
CountryAustralia
CityGold Coast
Period12/12/1615/12/16

Fingerprint

Urine
Proteins
Biomarkers

Keywords

  • urine excretory proteins
  • support vector machine recursive feature elimination
  • biomarkers of disease

Cite this

Wang, Y., Du, W., Liang, Y., Chen, X., Zhang, C., Pang, W., & Xu, Y. (2016). PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins. In J. Li, X. Li, S. Wang, J. Li, & Q. Z. Sheng (Eds.), Advanced Data Mining and Applications: 2th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings (pp. 714-725). (Lecture Notes in Artificial Intelligence (LNAI)). Springer International Publishing. https://doi.org/10.1007/978-3-319-49586-6_51

PUEPro : A Computational Pipeline for Prediction of Urine Excretory Proteins. / Wang, Yan; Du, Wei; Liang, Yanchun; Chen, Xin; Zhang, Chi; Pang, Wei; Xu, Ying .

Advanced Data Mining and Applications: 2th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings. ed. / Jinyan Li; Xue Li; Shuliang Wang; Jianxin Li; Quan Z. Sheng. Springer International Publishing, 2016. p. 714-725 (Lecture Notes in Artificial Intelligence (LNAI)).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, Y, Du, W, Liang, Y, Chen, X, Zhang, C, Pang, W & Xu, Y 2016, PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins. in J Li, X Li, S Wang, J Li & QZ Sheng (eds), Advanced Data Mining and Applications: 2th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings. Lecture Notes in Artificial Intelligence (LNAI), Springer International Publishing, pp. 714-725, ADMA 2016, Gold Coast, Australia, 12/12/16. https://doi.org/10.1007/978-3-319-49586-6_51
Wang Y, Du W, Liang Y, Chen X, Zhang C, Pang W et al. PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins. In Li J, Li X, Wang S, Li J, Sheng QZ, editors, Advanced Data Mining and Applications: 2th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings. Springer International Publishing. 2016. p. 714-725. (Lecture Notes in Artificial Intelligence (LNAI)). https://doi.org/10.1007/978-3-319-49586-6_51
Wang, Yan ; Du, Wei ; Liang, Yanchun ; Chen, Xin ; Zhang, Chi ; Pang, Wei ; Xu, Ying . / PUEPro : A Computational Pipeline for Prediction of Urine Excretory Proteins. Advanced Data Mining and Applications: 2th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings. editor / Jinyan Li ; Xue Li ; Shuliang Wang ; Jianxin Li ; Quan Z. Sheng. Springer International Publishing, 2016. pp. 714-725 (Lecture Notes in Artificial Intelligence (LNAI)).
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abstract = "A computational pipeline is developed to accurately predict urine excretoryproteins and the possible origins of the proteins. The novel contributionsof this study include: (i) a new method for predicting if a cellular protein isurine excretory based on unique features of proteins known to be urine excretory; and (ii) a novel method for identifying urinary proteins originating from the urinary system. By integrating these tools, our computational pipeline is capable of predicting the origin of a detected urinary protein, hence offering a novel tool for predicting potential biomarkers of a specific disease, which may have some of their proteins urine excreted. One application is presented for this prediction pipeline to demonstrate the effectiveness of its prediction. The pipeline and supplementary materials can be accessed at the following URL:http://csbl.bmb.uga.edu/PUEPro/",
keywords = "urine excretory proteins, support vector machine recursive feature elimination, biomarkers of disease",
author = "Yan Wang and Wei Du and Yanchun Liang and Xin Chen and Chi Zhang and Wei Pang and Ying Xu",
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AB - A computational pipeline is developed to accurately predict urine excretoryproteins and the possible origins of the proteins. The novel contributionsof this study include: (i) a new method for predicting if a cellular protein isurine excretory based on unique features of proteins known to be urine excretory; and (ii) a novel method for identifying urinary proteins originating from the urinary system. By integrating these tools, our computational pipeline is capable of predicting the origin of a detected urinary protein, hence offering a novel tool for predicting potential biomarkers of a specific disease, which may have some of their proteins urine excreted. One application is presented for this prediction pipeline to demonstrate the effectiveness of its prediction. The pipeline and supplementary materials can be accessed at the following URL:http://csbl.bmb.uga.edu/PUEPro/

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