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 proceedingPublished conference contribution

5 Citations (Scopus)
13 Downloads (Pure)


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
Number of pages12
ISBN (Electronic)978-3-319-49586-6
ISBN (Print)978-3-319-49585-9
Publication statusPublished - 2016
EventADMA 2016 - Gold Coast, Australia
Duration: 12 Dec 201615 Dec 2016

Publication series

NameLecture Notes in Artificial Intelligence (LNAI)


ConferenceADMA 2016
CityGold Coast

Bibliographical 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


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


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