基于双加权投票的蛋白质功能预测

doi:10.11896/j.issn.1002-137X.2019.04.035

Abstract

Abstract: Proteins are the essential molecules to accomplish important biological activities.It will greatly promote the advance of life science research and application to accurately grasp their functions.A tremendous amount of protein sequences has been generated with the development of high-throughput techniques.Thus,prediction of large-scale protein functions with computation technology has become one of the key tasks in bioinformatics today.Currently,the prediction method based on protein-protein interaction network,which is a research hotspot of protein function prediction,still has shortcomings at such aspects as reducing the impact of data noise,making full use of network topology characteristics,integrating multi-source data,and so on.In this paper,the Bi-Weighted Vote(BIWV) algorithm was proposed to predict protein functions,which combines the global topological similarity produced by Random Walk with Resistance (RWS) and the semantic similarity between terms.In addition,the Bi-Weighted Vote algorithm with pathway (BiWV-P) was presented by integrating the information of biological pathway.By using the data sets of saccharomyces cerevi-siae and homo sapiens,experiments were performed to compare TMC,UBiRW,ProHG,BiWV and BiWV-P.The experimental results indicate that BiWV algorithm and BiWV-P algorithm can predict protein functions effectively,and achieve higher micro-accuracy and micro-F1 than other algorithms in many data sets.

Key words: Biological pathway, Function prediction, Protein-protein interaction network, Random walk, Semantic similarity

CLC Number:

TP391

TANG Jia-qi, WU Jing-li, LIAO Yuan-xiu, WANG Jin-yan. Prediction of Protein Functions Based on Bi-weighted Vote[J].Computer Science, 2019, 46(4): 222-227.

References

[1]SCHWIKOWSKI B,UETZ P,FIELDS S.A network of protein-protein interactions in yeast[J].Nature Biotechnology,2000,18(12):1257-1261.
[2]HISHIGAKI H,NAKAI K,ONO T,et al.Assessment of prediction accuracy of protein function from protein-protein interaction data[J].Yeast,2001,18(6):523-531.
[3]CHUA H N,SUNG W K,WONG L.Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions[J].Bioinformatics,2006,22(13):1623-1630.
[4]CHRISTINE B,FRANÇOIS C,DAVID M,et al.Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network[J].Genome Biology,2003,5(1):6-18.
[5]NABIEVA E,JIM K,AGARWAL A,et al.Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps[J].Bioinformatics,2005,21(1):302-310.
[6]DENG M,TU Z,SUN F,et al.Mapping Gene Ontology to proteins based on protein-protein interaction data.[J].Bioinforma-tics,2004,20(6):895-902.
[7]VAZQUEZ A,FLAMMINI A,MARITAN A,et al.Global protein function prediction from protein-protein interaction,networks[J].Nature Biotechnology,2003,21(6):697-700.
[8]ZHANG X F,DAI D Q.A Framework for Incorporating Functional Interrelationships into Protein Function Prediction Algorithms[J].IEEE/ACM Transactions on Computational Biology &Bioinformatics,2012,9(3):740-753.
[9]WANG H,HUANG H,DING C.Function-Function Correlated Multi-Label Protein Function Prediction over Interaction Networks[C]∥International Conference on Research in Computational Molecular Biology.Berlin:Springer,2012:302-313.
[10]YU G,ZHU H,DOMENICONI C.Predicting protein functions using incomplete hierarchical labels[J].BMC Bioinformatics,2015,16(1):1-12.
[11]PENG W,WANG J,CHEN L,et al.Predicting protein functions by using unbalanced bi-random walk algorithm on protein-protein interaction network and functional interrelationship network[J].Current Protein & Peptide Science,2014,15(6):529-539.
[12]YU G,RANGWALA H,DOMENICONI C,et al.Protein Function Prediction using Multi-label Ensemble Classification[J].IEEE/ACM Transactions on Computational Biology & Bioinformatics,2013,10(4):1045-1057.
[13]LIU J,WANG J,YU G.Protein Function Prediction by Random Walks on a Hybrid Graph[J].Current Proteomics,2016,13(2):130-142.
[14]PRASAD A,SAHA S,CHATTERJEE P,et al.Protein Function Prediction from Protein Interaction Network Using Bottom-up L2L Apriori Algorithm[C]∥International Conference on Computational Intelligence,Communications,and Business Analytics.Singapore:Springer,2017:3-16.
[15]LICHTENBERG U D,JENSEN L J,BRUNAK S,et al.Dynamic Complex Formation During the Yeast Cell Cycle[J].Science,2005,307(5710):724-727.
[16]XIONG W,LIU H,GUAN J,et al.Protein function prediction by collective classification with explicit and implicit edges in protein-protein interaction networks[J].BMC Bioinformatics,2013,14(Suppl 12):4-16.
[17]COZZETTO D,BUCHAN D W,BRYSON K,et al.Protein function prediction by massive integration of evolutionary analyses and multiple data sources[J].BMC Bioinformatics,2013,14 (Suppl 3):1-11.
[18]CAO M,PIETRAS C M,FENG X,et al.New directions for diffusion-based network prediction of protein function:incorporating pathways with confidence[J].Bioinformatics,2014,30(12):219-227.
[19]PENG W,LI M,CHEN L,et al.Predicting protein functions by using unbalanced random walk algorithm on three biological networks[J].IEEE/ACM transactions on computational biology and bioinformatics,2017,14(2):360-369.
[20]LEI C,RUAN J.A novel link prediction algorithm for reconstructing protein-protein interaction networks by topological similarity[J].Bioinformatics,2013,29(3):355-364.
[21]WANG J Z,DU Z,PAYATTAKOOL R,et al.A new method to measure the semantic similarity of GO terms[J].Bioinforma-tics,2007,23(10):1274-1281.
[22]XENARIOS I,RICE D W,SALWINSKI L,et al.DIP:the database of interacting proteins.[J].Nucleic Acids Research,2000,32(1):289-291.
[23]OGATA H,GOTO S,SATO K,et al.KEGG:Kyoto Encyclopedia of Genes and Genomes.[J].Nucleic Acids Research,2000,27(1):29-34.
[24]ASHBURNER M,BALL C J,BOTSTEIN D,et al.Gene ontology:tool for the unification of biology.The Gene Ontology Consortium[J].Nature Genetics,2000,25(1):25-29.
[25]CARY M P,BADER G D,SANDER C.Pathway information for systems biology[J].FEBS Letters,2005,579(8):1815-1820.
[26]CONSORTIUM U P.The Universal Protein Resource (Uni- Prot) in 2010[J].Nucleic Acids Research,2010,38(Database issue):142-148.
[27]BIRNEY E.Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt[J].Nature Protocols,2009,4(8):1184-1191.
[28]TENENBAUM D.Client-side REST access to KEGG[EB/OL].http://rpackages.ianhowson.com/bioc/KEGGREST.
[29]ZHANG M L,ZHOU Z H.A Review on Multi-Label Learning Algorithms[J].IEEE Transactions on Knowledge & Data Engineering,2014,26(8):1819-1837.
[30]周志华.机器学习.北京:清华大学出版社,2016:23-33.
[31]GILLIS J,PAVLIDIS P.The Impact of Multifunctional Genes on “Guilt by Association” Analysis[J/OL].http://www.oalib.com/paper/134869@.W-vO7ywYxAs.

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Prediction of Protein Functions Based on Bi-weighted Vote

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