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

1 Citation (Scopus)

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/
Original languageEnglish
Title of host publicationADMA 2016: Advanced Data Mining and Applications
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
Event12th International Conference on Advanced Data Mining and Applications 2016 - Gold Coast, Australia, Gold Coast, Australia
Duration: 12 Dec 201615 Dec 2016

Publication series

Name Lecture Notes in Computer Science
Volume10086

Conference

Conference12th International Conference on Advanced Data Mining and Applications 2016
Abbreviated titleADMA 2016
CountryAustralia
CityGold Coast
Period12/12/1615/12/16

Keywords

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

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