Title: SMISS: a protein function prediction server by integrating multiple sources

Authors: Renzhi Cao; Zhaolong Zhong; Jianlin Cheng

Addresses: Pacific Lutheran University, 12180 Park Ave S, Tacoma, WA 98447, USA ' University of Missouri-Columbia, 230 Jesse Hall, Columbia, MO 65211, USA ' University of Missouri-Columbia, 230 Jesse Hall, Columbia, MO 65211, USA

Abstract: SMISS is a novel web server for protein function prediction. Three different predictors can be selected for different usage. It integrates different sources to improve the protein function prediction accuracy, including the query protein sequence, protein-protein interaction network, gene-gene interaction network and the rules mined from protein function associations. SMISS automatically switch to ab initio protein function prediction based on the query sequence when there is no homolog's in the database. It takes fasta format sequences as input; and several sequences can be submitted together without influencing the computation speed too much. PHP and Perl are two primary programming language used in the server. The CodeIgniter MVC PHP web framework and bootstrap front-end framework are used for building the server. It can be used in different platforms in standard web browser, such as Windows, Mac OS X, Linux and iOS. No plug-ins is needed for our website (availability: http://tulip.rnet.missouri.edu/profunc/).

Keywords: protein function prediction; data integration; spatial gene-gene interaction network; protein-protein interaction network; chromosome conformation capturing.

DOI: 10.1504/IJCIBSB.2020.106859

International Journal of Computational Intelligence in Bioinformatics and Systems Biology, 2020 Vol.2 No.1, pp.22 - 30

Accepted: 20 May 2017
Published online: 24 Apr 2020 *

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