@inproceedings{a0ac6142ab40481a906841997ab9fcda,
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).; 12th International Conference on Advanced Data Mining and Applications 2016, 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 Computer Science ",
publisher = "Springer",
pages = "714--725",
editor = "Jinyan Li and Xue Li and Shuliang Wang and Jianxin Li and Sheng, {Quan Z.}",
booktitle = "ADMA 2016: Advanced Data Mining and Applications",
}