Affiliation:
1. College of Computer Science and Technology in the Jilin University
2. College of Computer Science and Technology in Changchun University
3. College of Computer Science and Technology in the Changchun University
4. Department of Computer Science and Engineering in the University of Nebraska-Lincoln
Abstract
Abstract
Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein–protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.
Funder
National Natural Science Foundation of China
Development Project of Jilin Province of China
Jilin Provincial Key Laboratory of Big Date Intelligent Computing
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Cited by
36 articles.
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