PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins

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

doi:10.1007/978-3-319-49586-6_51

PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins

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

Computing Science

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

3 Citations (Scopus)

7 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 language	English
Title of host publication	Advanced Data Mining and Applications
Subtitle of host publication	2th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings
Editors	Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng
Publisher	Springer International Publishing
Pages	714-725
Number of pages	12
ISBN (Electronic)	978-3-319-49586-6
ISBN (Print)	978-3-319-49585-9
DOIs	https://doi.org/10.1007/978-3-319-49586-6_51
Publication status	Published - 2016
Event	ADMA 2016 - Gold Coast, Australia Duration: 12 Dec 2016 → 15 Dec 2016

Publication series

Name	Lecture Notes in Artificial Intelligence (LNAI)
Publisher	Springer

Conference

Conference	ADMA 2016
Country/Territory	Australia
City	Gold Coast
Period	12/12/16 → 15/12/16

Keywords

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

Access to Document

10.1007/978-3-319-49586-6_51

Accepted Manuscript
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-49586-6_51
Accepted author manuscript, 602 KBLicence: Unspecified

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 et al.

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 proceeding › Published conference 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)). Epub 2016 Nov 13. doi: 10.1007/978-3-319-49586-6_51

Wang, Yan ; Du, Wei ; Liang, Yanchun et al. / 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)).

@inproceedings{796c0813d1034c189b72400b09580e83,

title = "PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins",

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",

note = "This work is supported by the National Natural Science Foundation of China (Grant Nos. 81320108025, 61402194, 61572227), Development Project of Jilin Province of China (20140101180JC) and China Postdoctoral Science Foundation (2014T70291).; ADMA 2016 ; Conference date: 12-12-2016 Through 15-12-2016",

year = "2016",

doi = "10.1007/978-3-319-49586-6_51",

language = "English",

isbn = "978-3-319-49585-9",

series = "Lecture Notes in Artificial Intelligence (LNAI)",

publisher = "Springer International Publishing",

pages = "714--725",

editor = "Jinyan Li and Xue Li and Shuliang Wang and Jianxin Li and Sheng, {Quan Z. }",

booktitle = "Advanced Data Mining and Applications",

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AU - Wang, Yan

AU - Du, Wei

AU - Liang, Yanchun

AU - Chen, Xin

AU - Zhang, Chi

AU - Pang, Wei

AU - Xu, Ying

N1 - This work is supported by the National Natural Science Foundation of China (Grant Nos. 81320108025, 61402194, 61572227), Development Project of Jilin Province of China (20140101180JC) and China Postdoctoral Science Foundation (2014T70291).

PY - 2016

Y1 - 2016

N2 - 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/

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/

KW - urine excretory proteins

KW - support vector machine recursive feature elimination

KW - biomarkers of disease

U2 - 10.1007/978-3-319-49586-6_51

DO - 10.1007/978-3-319-49586-6_51

M3 - Published conference contribution

SN - 978-3-319-49585-9

T3 - Lecture Notes in Artificial Intelligence (LNAI)

SP - 714

EP - 725

BT - Advanced Data Mining and Applications

A2 - Li, Jinyan

A2 - Li, Xue

A2 - Wang, Shuliang

A2 - Li, Jianxin

A2 - Sheng, Quan Z.

PB - Springer International Publishing

Y2 - 12 December 2016 through 15 December 2016

ER -

PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins

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