Generic placeholder image

Current Drug Metabolism


ISSN (Print): 1389-2002
ISSN (Online): 1875-5453

Review Article

Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches

Author(s): Nantao Zheng, Kairou Wang, Weihua Zhan and Lei Deng*

Volume 20, Issue 3, 2019

Page: [177 - 184] Pages: 8

DOI: 10.2174/1389200219666180829121038

Price: $65


Background: Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions.

Methods: In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods.

Results: We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions.

Conclusion: The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.

Keywords: Virus-host protein-protein interactions, computational methods, feature extraction, feature representation, machine learning, deep learning.

Graphical Abstract
Arnold, R.; Boonen, K.; Sun, M.G.; Kim, P.M. Computational analysis of interactomes: Current and future perspectives for bioinformatics approaches to model the host-pathogen interaction space. Methods, 2012, 57(4), 508-518.
Zhou, H.; Jin, J.; Wong, L. Progress in computational studies of host-pathogen interactions. J. Bioinform. Comput. Biol., 2013, 11(02), 1230001.
Tastan, O.; Qi, Y.; Carbonell, J.G.; Klein-Seetharaman, J. Prediction of interactions between HIV-1 and human proteins by information integration.In Biocomputing 2009; World Scientific, 2009, pp. 516-527.
Qi, Y.; Tastan, O.; Carbonell, J.G.; Klein-Seetharaman, J.; Weston, J. Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins. Bioinformatics, 2010, 26(18), i645-i652.
Dyer, M.D.; Murali, T.; Sobral, B.W. Supervised learning and prediction of physical interactions between human and HIV proteins. Infect. Genet. Evol., 2011, 11(5), 917-923.
Mei, S. Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins. PLoS One, 2013, 8(11), e79606.
Doolittle, J.M.; Gomez, S.M. Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens. Virol. J., 2010, 7(1), 82.
Evans, P.; Dampier, W.; Ungar, L.; Tozeren, A. Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs. BMC Med. Genomics, 2009, 2(1), 27.
Mukhopadhyay, A.; Maulik, U.; Bandyopadhyay, S.; Eils, R. In: Mining association rules from HIV-human protein interactions, Proceedings of the 2010 International Conference on Systems in Medicine and Biology, Kharagpur, IN, December 16-18. 2010.
Mukhopadhyay, A.; Maulik, U.; Bandyopadhyay, S. A novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions. PLoS One, 2012, 7(4), e32289.
Mondal, K.C.; Pasquier, N.; Mukhopadhyay, A.; Maulik, U.; Bandhopadyay, S. In: A new approach for association rule mining and bi-clustering using formal concept analysis, Proceedings of the 2012 International Workshop on Machine Learning and Data Mining in Pattern Recognition, Berlin, GER, July 13-20. 2012.
Mukhopadhyay, A.; Ray, S.; Maulik, U. Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach. BMC Bioinformatics, 2014, 15(1), 26.
Segura-Cabrera, A.; García-Pérez, C.A.; Guo, X.; Rodríguez-Pérez, M.A. A viral-human interactome based on structural motif-domain interactions captures the human infectome. PLoS One, 2013, 8(8), e71526.
Kshirsagar, M.; Carbonell, J.; Klein-Seetharaman, J. Multitask learning for host-pathogen protein interactions. Bioinformatics, 2013, 29(13), i217-i226.
Cao, H.; Zhang, Y.; Zhao, J.; Zhu, L.; Wang, Y.; Li, J.; Feng, Y-M.; Zhang, N. Prediction of the Ebola virus infection related human genes using protein-protein interaction network. Comb. Chem. High Throughput Screen., 2017, 20(7), 638-646.
Halder, A.K.; Dutta, P.; Kundu, M.; Basu, S.; Nasipuri, M. Review of computational methods for virus-host protein interaction prediction: A case study on novel Ebola-human interactions. Brief. Funct. Genomics, 2018, 17(6), 381-391.
Barman, R.K.; Saha, S.; Das, S. Prediction of interactions between viral and host proteins using supervised machine learning methods. PLoS One, 2014, 9(11), e112034.
Cui, G.; Fang, C.; Han, K. In: Prediction of protein-protein interactions between viruses and human by an SVM model, Proceedings of the 2011 International Conference on Intelligent Computing, Zhengzhou, CN, August 11-14. 2011.
Kim, B.; Alguwaizani, S.; Zhou, X.; Huang, D-S.; Park, B.; Han, K. An improved method for predicting interactions between virus and human proteins. J. Bioinform. Comput. Biol., 2017, 15(1), 1650024.
Zheng, L-L.; Li, C.; Ping, J.; Zhou, Y.; Li, Y.; Hao, P. The domain landscape of virus-host interactomes. BioMed Res. Int., 2014, 2014, 867235.
Emamjomeh, A.; Goliaei, B.; Zahiri, J.; Ebrahimpour, R. Predicting protein-protein interactions between human and hepatitis C virus via an ensemble learning method. Mol. Biosyst., 2014, 10(12), 3147-3154.
Chiang, A.W.; Wu, W.Y.; Wang, T.; Hwang, M-J. Identification of entry factors involved in hepatitis C virus infection based on host-mimicking short linear motifs. PLOS Comput. Biol., 2017, 13(1), e1005368.
Doolittle, J.M.; Gomez, S.M. Mapping protein interactions between Dengue virus and its human and insect hosts. PLoS Negl. Trop. Dis., 2011, 5(2), e954.
De Chassey, B.; Meyniel-Schicklin, L.; Aublin-Gex, A.; Navratil, V.; Chantier, T.; Andre, P.; Lotteau, V. Structure homology and interaction redundancy for discovering virus–host protein interactions. EMBO Rep., 2013, 14(10), 938-944.
Eng, C.L.; Tong, J.C.; Tan, T.W. Predicting host tropism of influenza A virus proteins using random forest. BMC Med. Genomics, 2014, 7(3), S1.
Zeng, J.; Li, D.; Wu, Y.; Zou, Q.; Liu, X. An empirical study of features fusion techniques for protein-protein interaction prediction. Curr. Bioinform., 2016, 11(1), 4-12.
Sanger, F. The arrangement of amino acids in proteins.In Adv. Protein Chem; Elsevier: Amsterdam, 1952, Vol. 7, pp. 1-67.
Anfinsen, C.B. Principles that govern the folding of protein chains. Science, 1973, 181(4096), 223-230.
Shen, J.; Zhang, J.; Luo, X.; Zhu, W.; Yu, K.; Chen, K.; Li, Y.; Jiang, H. Predicting protein-protein interactions based only on sequences information. Proc. Natl. Acad. Sci. USA, 2007, 104(11), 4337-4341.
Yu, J.; Guo, M.; Needham, C.J.; Huang, Y.; Cai, L.; Westhead, D.R. Simple sequence-based kernels do not predict protein–protein interactions. Bioinformatics, 2010, 26(20), 2610-2614.
Dyer, M.D.; Murali, T.; Sobral, B.W. Computational prediction of host-pathogen protein-protein interactions. Bioinformatics, 2007, 23(13), i159-i166.
Hunt, T. Protein sequence motifs involved in recognition and targeting: A new series. Trends Biochem. Sci., 1990, 15, 305.
Kadaveru, K.; Vyas, J.; Schiller, M.R. Viral infection and human disease-insights from minimotifs. Front. Biosci., 2008, 13, 6455-6471.
Tonikian, R.; Zhang, Y.; Sazinsky, S.L.; Currell, B.; Yeh, J-H.; Reva, B.; Held, H.A.; Appleton, B.A.; Evangelista, M.; Wu, Y. A specificity map for the PDZ domain family. PLoS Biol., 2008, 6(9), e239.
Shelton, H.; Harris, M. Hepatitis C virus NS5A protein binds the SH3 domain of the Fyn tyrosine kinase with high affinity: Mutagenic analysis of residues within the SH3 domain that contribute to the interaction. Virol. J., 2008, 5(1), 24.
Diella, F.; Haslam, N.; Chica, C.; Budd, A.; Michael, S.; Brown, N.P.; Travé, G.; Gibson, T.J. Understanding eukaryotic linear motifs and their role in cell signaling and regulation. Front. Biosci., 2008, 13(6580), 603.
Neduva, V.; Russell, R.B. Peptides mediating interaction networks: New leads at last. Curr. Opin. Biotechnol., 2006, 17(5), 465-471.
Becerra, A.; Bucheli, V.A.; Moreno, P.A. Prediction of virus-host protein-protein interactions mediated by short linear motifs. BMC Bioinformatics, 2017, 18(1), 163.
Via, A.; Gould, C.M.; Gemünd, C.; Gibson, T.J.; Helmer-Citterich, M. A structure filter for the eukaryotic linear motif resource. BMC Bioinformatics, 2009, 10(1), 351.
Zhang, A.; He, L.; Wang, Y. Prediction of GCRV virus-host protein interactome based on structural motif-domain interactions. BMC Bioinformatics, 2017, 18(1), 145.
Deng, L.; Zhang, Q.C.; Chen, Z.; Meng, Y.; Guan, J.; Zhou, S.; Pred, H.S. A web server for predicting protein-protein interaction hot spots by using structural neighborhood properties. Nucleic Acids Res., 2014, 42(W1), W290-W295.
Petrey, D.; Chen, T.S.; Deng, L.; Garzon, J.I.; Hwang, H.; Lasso, G.; Lee, H.; Silkov, A.; Honig, B. Template-based prediction of protein function. Curr. Opin. Struct. Biol., 2015, 32, 33-38.
Zhang, Q.C.; Petrey, D.; Deng, L.; Qiang, L.; Shi, Y.; Thu, C.A.; Bisikirska, B.; Lefebvre, C.; Accili, D.; Hunter, T. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature, 2012, 490(7421), 556.
Zhang, Q.C.; Petrey, D.; Garzon, J.I.; Deng, L.; Honig, B. PrePPI: A structure-informed database of protein-protein interactions. Nucleic Acids Res., 2012, 41(D1), D828-D833.
Garzón, J.I.; Deng, L.; Murray, D.; Shapira, S.; Petrey, D.; Honig, B. A computational interactome and functional annotation for the human proteome. eLife, 2016, 5, e18715.
Wei, L.; Zou, Q.; Liao, M.; Lu, H.; Zhao, Y. A novel machine learning method for cytokine-receptor interaction prediction. Comb. Chem. High Throughput Screen., 2016, 19(2), 144-152.
Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn., 1995, 20(3), 273-297.
Eid, F-E.; ElHefnawi, M.; Heath, L.S. DeNovo: Virus-host sequence-based protein-protein interaction prediction. Bioinformatics, 2015, 32(8), 1144-1150.
Kumar, M.; Gromiha, M.M.; Raghava, G.P. Identification of DNA-binding proteins using support vector machines and evolutionary profiles. BMC Bioinformatics, 2007, 8(1), 463.
Yu, X.; Cao, J.; Cai, Y.; Shi, T.; Li, Y. Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines. J. Theor. Biol., 2006, 240(2), 175-184.
Liang, Z-Y.; Lai, H-Y.; Yang, H.; Zhang, C-J.; Yang, H.; Wei, H-H.; Chen, X-X.; Zhao, Y-W.; Su, Z-D.; Li, W-C. Pro54DB: a database for experimentally verified sigma-54 promoters. Bioinformatics, 2017, 33(3), 467-469.
Chen, W.; Yang, H.; Feng, P.; Ding, H.; Lin, H. iDNA4mC: Identifying DNA N4-methylcytosine sites based on nucleotide chemical properties. Bioinformatics, 2017, 33(22), 3518-3523.
Chen, W.; Tang, H.; Lin, H. MethyRNA: A web server for identification of N6-methyladenosine sites. J. Biomol. Struct. Dyn., 2017, 35(3), 683-687.
Yang, H.; Tang, H.; Chen, X-X.; Zhang, C-J.; Zhu, P-P.; Ding, H.; Chen, W.; Lin, H. Identification of secretory proteins in mycobacterium tuberculosis using pseudo amino acid composition. BioMed Res. Int., 2016, 2016, 5413903.
Chen, X-X.; Tang, H.; Li, W-C.; Wu, H.; Chen, W.; Ding, H.; Lin, H. Identification of bacterial cell wall lyases via pseudo amino acid composition. BioMed Res. Int., 2016, 2016, 1654623.
Qiu, W-R.; Sun, B-Q.; Tang, H.; Huang, J.; Lin, H. Identify and analysis crotonylation sites in histone by using support vector machines. Artif. Intell. Med., 2017, 83, 75-81.
Lin, H.; Liang, Z.-Y.; Tang, H.; Chen, W. Identifying sigma70 promoters with novel pseudo nucleotide composition. IEEE/ACM Trans. Comput. Biol. Bioinform, 2017.
Lai, H-Y.; Chen, X-X.; Chen, W.; Tang, H.; Lin, H. Sequence-based predictive modeling to identify cancerlectins. Oncotarget, 2017, 8(17), 28169.
De Chassey, B.; Navratil, V.; Tafforeau, L.; Hiet, M.; Aublin-Gex, A.; Agaugue, S.; Meiffren, G.; Pradezynski, F.; Faria, B.; Chantier, T. Hepatitis C virus infection protein network. Mol. Syst. Biol., 2008, 4(1), 230.
Mei, S.; Zhu, H. A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks. Sci. Rep., 2015, 5, 8034.
Opitz, D.; Maclin, R. Popular ensemble methods: An empirical study. J. Artif. Intell. Res., 1999, 11, 169-198.
Polikar, R. Ensemble based systems in decision making. IEEE Circuits Syst. Mag., 2006, 6(3), 21-45.
Rokach, L. Ensemble-based classifiers. Artif. Intell. Rev., 2010, 33(1-2), 1-39.
Lin, C.; Chen, W.; Qiu, C.; Wu, Y.; Krishnan, S.; Zou, Q. LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy. Neurocomputing, 2014, 123, 424-435.
Mitchell, T.M. Machine Learning, 1st ed; McGraw Hill: Burr Ridge, IL, 1997.
Breiman, L. Bagging predictors. Mach. Learn., 1996, 24(2), 123-140.
Kearns, M. Thoughts on hypothesis boosting. Unpubl. Manus., 1988, 45, 105.
Freund, Y.; Schapire, R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci., 1997, 55(1), 119-139.
Friedman, J.; Hastie, T.; Tibshirani, R. Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat., 2000, 28(2), 337-407.
Fan, C.; Liu, D.; Huang, R.; Chen, Z.; Deng, L. PredRSA: A gradient boosted regression trees approach for predicting protein solvent accessibility. BMC Bioinformatics, 2016, 17(Suppl. 1), S8.
Pan, Y.; Liu, D.; Deng, L. Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties. PLoS One, 2017, 12(6), e0179314.
Tang, Y.; Liu, D.; Wang, Z.; Wen, T.; Deng, L. A boosting approach for prediction of protein-RNA binding residues. BMC Bioinformatics, 2017, 18(13), 465.
Hoeting, J.A.; Madigan, D.; Raftery, A.E.; Volinsky, C.T. Bayesian model averaging: A tutorial. Stat. Sci., 1999, 14(4), 382-401.
Monteith, K.; Carroll, J.L.; Seppi, K.; Martinez, T. In: Turning Bayesian model averaging into Bayesian model combination, Proceedings of the 2011 International Joint Conference on Neural Network, San Jose, California, USA, July 31-August 5. 2011.
Wolpert, D.H. Stacked generalization. Neural Netw., 1992, 5(2), 241-259.
Tan, A.C.; Gilbert, D. In: Ensemble machine learning on gene expression data for cancer classification, Proceedings of New Zealand Bioinformatics Conference, Te Papa, Wellington, NZ, February 13-14. 2003.
Liu, B.; Wang, S.; Long, R.; Chou, K-C. iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics, 2016, 33(1), 35-41.
Shen, H-B.; Chou, K-C. Ensemble classifier for protein fold pattern recognition. Bioinformatics, 2006, 22(14), 1717-1722.
Wan, S.; Duan, Y.; Zou, Q. HPSLPred: An ensemble multi-label classifier for human protein subcellular location prediction with imbalanced source. Proteomics, 2017, 17(17-18), 1700262.
Deng, L.; Chen, Z. An integrated framework for functional annotation of protein structural domains. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2015, 12(4), 902-913.
Pan, Y.; Wang, Z.; Zhan, W.; Deng, L. Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach. Bioinformatics, 2017, 34(9), 1473-1480.
Wu, J.; Liu, H.; Duan, X.; Ding, Y.; Wu, H.; Bai, Y.; Sun, X. Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature. Bioinformatics, 2008, 25(1), 30-35.
Chen, X-W.; Liu, M. Prediction of protein-protein interactions using random decision forest framework. Bioinformatics, 2005, 21(24), 4394-4400.
Liu, Z-P.; Wu, L-Y.; Wang, Y.; Zhang, X-S.; Chen, L. Prediction of protein-RNA binding sites by a random forest method with combined features. Bioinformatics, 2010, 26(13), 1616-1622.
Zhang, C-J.; Tang, H.; Li, W-C.; Lin, H.; Chen, W.; Chou, K-C. iOri-Human: Identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget, 2016, 7(43), 69783.
Qi, Y.; Bar-Joseph, Z.; Klein-Seetharaman, J. Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins, 2006, 63(3), 490-500.
Lin, N.; Wu, B.; Jansen, R.; Gerstein, M.; Zhao, H. Information assessment on predicting protein-protein interactions. BMC Bioinformatics, 2004, 5(1), 154.
Pratt, L.Y. Discriminability-based transfer between neural networks In: Advances in neural information processing systems, Colorado, USA. 1993, pp. 204-211.
Evgeniou, T.; Pontil, M. In: Regularized multi--task learning, Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining, Seattle, WA, USA, August 22-25. 2004.
Baxter, J. Theoretical models of learning to learn.Learning to learn; Springer: New York City, 1998, pp. 71-94.
Xu, Q.; Yang, Q. A survey of transfer and multitask learning in bioinformatics. J. Comput. Sci. Eng., 2011, 5(3), 257-268.
Boeckmann, B.; Bairoch, A.; Apweiler, R.; Blatter, M-C.; Estreicher, A.; Gasteiger, E.; Martin, M.J.; Michoud, K.; O’donovan, C.; Phan, I. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res., 2003, 31(1), 365-370.
Barrell, D.; Dimmer, E.; Huntley, R.P.; Binns, D.; O’donovan, C.; Apweiler, R. The GOA database in 2009-an integrated gene ontology annotation resource. Nucleic Acids Res., 2008, 37(Suppl. 1), D396-D403.
Mei, S.; Zhu, H. Computational reconstruction of proteome-wide protein interaction networks between HTLV retroviruses and Homo sapiens. BMC Bioinformatics, 2014, 15(1), 245.
Chapelle, O.; Scholkopf, B.; Zien, A. Semi-supervised learning. IEEE Trans. Neural Netw., 2009, 20(3), 542-542.
Zhu, X. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin- Madison. 2005.
Zhu, X.; Goldberg, A.B. Introduction to semi-supervised learning Synthesis lectures on artificial intelligence and machine learning, Morgan and Claypool Publishers: California. 2009, Vol. 3(1), pp. 1- 130.
Hady, M.F.A.; Schwenker, F. Semi-supervised learning.In Handbook on Neural Information Processing; Springer: New York City, 2013, pp. 215-239.
Xia, Z.; Wu, L-Y.; Zhou, X.; Wong, S.T. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol., 2010, 4(Suppl. 2), S8.
Deng, L.; Guan, J-H.; Dong, Q-W.; Zhou, S-G.; Semi, H.S. An iterative semi-supervised approach for predicting proteinprotein interaction hot spots. Protein Pept. Lett., 2011, 18(9), 896-905.
Fu, W.; Sanders-Beer, B.E.; Katz, K.S.; Maglott, D.R.; Pruitt, K.D.; Ptak, R.G. Human immunodeficiency virus type 1, human protein interaction database at NCBI. Nucleic Acids Res., 2008, 37(Suppl. 1), D417-D422.
Deng, L.; Yu, D. Deep learning: Methods and applications. Foundations and Trends® in Signal Processing, Now Publishers Inc: Netherlands. 2014, Vol. 7(3-4), pp. 197-387.
Chen, H.; Shen, J.; Wang, L.; Song, J. In: Collaborative data analytics towards prediction on pathogen-host protein-protein interactions, Proceedings of the 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD), Wellington, NZ, April 26-28. 2017.
Vincent, P.; Larochelle, H.; Bengio, Y.; Manzagol, P-A. In: Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th international conference on Machine learning, Helsinki, FI, July 5-8. 2008.
Domínguez-Almendros, S.; Benítez-Parejo, N.; Gonzalez-Ramirez, A. Logistic regression models. Allergol. Immunopathol. (Madr.), 2011, 39(5), 295-305.
Liu, H.; Sun, J.; Guan, J.; Zheng, J.; Zhou, S. Improving compound–protein interaction prediction by building up highly credible negative samples. Bioinformatics, 2015, 31(12), i221-i229.
Tian, K.; Shao, M.; Wang, Y.; Guan, J.; Zhou, S. Boosting compound-protein interaction prediction by deep learning. Methods, 2016, 110, 64-72.
Xiao, Y.; Zhang, J.; Deng, L. Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks. Sci. Rep., 2017, 7(1), 3664.
Zhang, J.; Zhang, Z.; Chen, Z.; Deng, L. Integrating multiple heterogeneous networks for novel lncRNA-disease association inference. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2019, 16(2), 396-406.
Zhang, J.; Zhang, Z.; Wang, Z.; Liu, Y.; Deng, L. Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification. Bioinformatics, 2017, 34(10), 1750-1757.
Zhang, Z.; Zhang, J.; Fan, C.; Tang, Y.; Deng, L. KATZLGO: Large-scale prediction of LncRNA functions by using the KATZ measure based on multiple networks. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2019, 16(2), 407-416.
Yu, G.; Fu, G.; Wang, J.; Zhao, Y. NewGOA: Predicting new GO annotations of proteins by bi-random walks on a hybrid graph. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2018, 15(4), 1390-1402.

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy