Predicting Protein Function via Semantic Integration of Multiple Networks

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Guoxian YuHailong Zhu

Abstract

Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically integrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sou...Continue Reading

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Citations

Jan 4, 2018·BMC Bioinformatics·Zhen TianShiming Wang
Jun 24, 2017·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Guoxian YuYingwen Zhao
May 12, 2020·Frontiers in Genetics·Yingwen ZhaoGuoxian Yu
Jul 27, 2017·BioMed Research International·Xianchun ZouGuoxian Yu
Dec 24, 2018·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Jingpu Zhang, Lei Deng
Jan 4, 2018·Journal of Biomedical Semantics·Zhen TianYin Zhang
Apr 7, 2020·Briefings in Bioinformatics·Jiajie PengXuequn Shang
Jun 19, 2019·BMC Bioinformatics·Stefano TesoAndrea Passerini
Mar 10, 2021·Briefings in Functional Genomics·Qingfeng ChenYi-Ping Phoebe Chen

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