PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10086)

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/.

Keywords

Urine excretory proteins Support vector machine recursive feature elimination Biomarkers of disease 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyChangchunChina
  2. 2.College of Public HealthJilin UniversityChangchunChina
  3. 3.School of Natural and Computing SciencesUniversity of AberdeenAberdeenUK
  4. 4.Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensUSA

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