SMISS: a protein function prediction server by integrating multiple sources
by Renzhi Cao; Zhaolong Zhong; Jianlin Cheng
International Journal of Computational Intelligence in Bioinformatics and Systems Biology (IJCIBSB), Vol. 2, No. 1, 2020

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/).

Online publication date: Fri, 24-Apr-2020

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