Skip to main content
SpringerLink
Log in

Supervised Gene Function Prediction Using Spectral Clustering on Gene Co-expression Networks

  • Conference paper
  • First Online:
Complex Networks & Their Applications X (COMPLEX NETWORKS 2021)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1073))

Included in the following conference series:

Abstract

Gene annotation addresses the problem of predicting unknown functions that are associated to the genes of a specific organism (e.g., biological processes). Despite recent advances, the cost and time demanded by annotation procedures that rely largely on in vivo biological experiments remain prohibitively high. This paper presents an in silico approach to the annotation of genes that follows a network-based representation, and combines techniques from multivariate statistics (spectral clustering) and machine learning (gradient boosting). Spectral clustering is used to enrich the gene co-expression network (GCN) with currently known gene annotations. Gradient boosting is trained on features of the GCN to build an estimator of the probability that a gene is involved in a given biological process. The proposed approach is applied to a case study on Zea mays, one of the world’s most dominant and productive crop. Broadly speaking, the main results illustrate how computational experimentation narrows down the time and costs in efforts to annotate the functions of genes. More specifically, the results highlight the importance of network science, multivariate statistics, and machine learning techniques in reducing types I and II prediction errors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(10), 281–305 (2012)

    MathSciNet  MATH  Google Scholar 

  2. Carbon, S., Mungall, C.: Gene Ontology Data Archive, July 2018. Type: dataset

    Google Scholar 

  3. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  MATH  Google Scholar 

  4. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)

    Google Scholar 

  5. Cho, H., Berger, B., Peng, J.: Diffusion component analysis: unraveling functional topology in biological networks. In: Przytycka, T.M. (ed.) RECOMB 2015. LNCS, vol. 9029, pp. 62–64. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16706-0_9

    Chapter  Google Scholar 

  6. Cho, H., Berger, B., Peng, J.: Compact integration of multi-network topology for functional analysis of genes. Cell Syst. 3(6), 540-548.e5 (2016)

    Article  Google Scholar 

  7. Cruz, D.F., et al.: Using single-plant-omics in the field to link maize genes to functions and phenotypes. Mol. Syst. Biol. 16(12), e9667 (2020)

    Article  Google Scholar 

  8. Deng, M., Zhang, K., Mehta, S., Chen, T., Sun, F.: Prediction of protein function using protein-protein interaction data. J. Comput. Biol. 10(6), 947–960 (2003)

    Article  Google Scholar 

  9. Gene Ontology Consortium: The gene ontology resource: 20 years and still GOing strong. Nucl. Acids Res. 47(D1), D330–D338 (2019)

    Article  Google Scholar 

  10. Jensen, R.A.: Orthologs and paralogs - we need to get it right. Genom. Biol. 2(8), 1–3 (2001)

    Article  Google Scholar 

  11. Jia, H., Ding, S., Xu, X., Nie, R.: The latest research progress on spectral clustering. Neural Comput. Appl. 24(7), 1477–1486 (2013). https://doi.org/10.1007/s00521-013-1439-2

    Article  Google Scholar 

  12. Ju, W., Li, J., Yu, W., Zhang, R.: iGraph: an incremental data processing system for dynamic graph. Front. Comput. Sci. 10(3), 462–476 (2016)

    Article  Google Scholar 

  13. Lundberg, S., Lee, S.-I.: A Unified Approach to Interpreting Model Predictions. arXiv:1705.07874, November 2017

  14. Lundberg, S.M., et al.: From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2(1), 2522–5839 (2020)

    Article  Google Scholar 

  15. Luo, F., et al.: Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory. BMC Bioinform. 8(1), 299 (2007)

    Article  Google Scholar 

  16. Murugesan, N., Cho, I., Tortora, C.: Benchmarking in cluster analysis: a study on spectral clustering, DBSCAN, and K-Means. In: Chadjipadelis, T., Lausen, B., Markos, A., Lee, T.R., Montanari, A., Nugent, R. (eds.) IFCS 2019. SCDAKO, pp. 175–185. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-60104-1_20

    Chapter  Google Scholar 

  17. Musungu, B., Bhatnagar, D., Brown, R.L., Fakhoury, A.M., Geisler, M.: A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize. Front. Genet. 6 (2015)

    Google Scholar 

  18. Obayashi, T., Aoki, Y., Tadaka, S., Kagaya, Y., Kinoshita, K.: ATTED-II in 2018: a plant coexpression database based on investigation of the statistical property of the mutual rank index. Plant Cell Physiol. 59(1), e3–e3 (2018)

    Article  Google Scholar 

  19. Obayashi, T., Kinoshita, K.: COXPRESdb: a database to compare gene coexpression in seven model animals. Nucl. Acids Res. 39, D1016–D1022 (2011)

    Article  Google Scholar 

  20. Oti, M., van Reeuwijk, J., Huynen, M.A., Brunner, H.G.: Conserved co-expression for candidate disease gene prioritization. BMC Bioinform. 9(1), 208 (2008)

    Article  Google Scholar 

  21. Petsko, G.A.: Guilt by association. Genom. Biol. 10(4), 104 (2009)

    Article  Google Scholar 

  22. Rehman, S.U., Asghar, S., Fong, S., Sarasvady, S.: DBSCAN: past, present and future. In: The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014), pp. 232–238, Bangalore, India, February 2014

    Google Scholar 

  23. Rodriguez, M.Z., et al.: Clustering algorithms: a comparative approach. PLoS One 14(1), e0210236 (2019)

    Article  MathSciNet  Google Scholar 

  24. Romero, M., Finke, J., Quimbaya, M., Rocha, C.: In-silico gene annotation prediction using the co-expression network structure. In: Cherifi, H., Gaito, S., Mendes, J.F., Moro, E., Rocha, L.M. (eds.) COMPLEX NETWORKS 2019. SCI, vol. 882, pp. 802–812. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36683-4_64

    Chapter  Google Scholar 

  25. Rust, A.G., Mongin, E., Birney, E.: Genome annotation techniques: new approaches and challenges. Drug Discov. Today 7(11), S70–S76 (2002)

    Article  Google Scholar 

  26. Valentini, G.: True path rule hierarchical ensembles. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 232–241. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02326-2_24

    Chapter  Google Scholar 

  27. van Dam, S., Võsa, U., van der Graaf, A., Franke, L., de Magalhães, J.P.: Gene co-expression analysis for functional classification and gene-disease predictions. Briefings Bioinform. 19(4), 139 (2017)

    Google Scholar 

  28. Vandepoele, K., Quimbaya, M., Casneuf, T., De Veylder, L., Van de Peer, Y.: Unraveling transcriptional control in Arabidopsis using CIS-regulatory elements and coexpression networks. Plant Physiology 150(2), 535–546 (2009)

    Article  Google Scholar 

  29. Yandell, M., Ence, D.: A beginner’s guide to eukaryotic genome annotation. Nat. Rev. Genet. 13(5), 329–342 (2012)

    Article  Google Scholar 

  30. Yon Rhee, S., Wood, V., Dolinski, K., Draghici, S.: Use and misuse of the gene ontology annotations. Nat. Rev. Genet. 9(7), 509–515 (2008)

    Article  Google Scholar 

  31. Zhou, G., Wang, J., Zhang, X., Guo, M., Yu, G.: Predicting functions of maize proteins using graph convolutional network. BMC Bioinform. 21(S16), 420 (2020)

    Article  Google Scholar 

  32. Zhou, Y., Young, J.A., Santrosyan, A., Chen, K., Yan, S.F., Winzeler, E.A.: In silico gene function prediction using ontology-based pattern identification. Bioinformatics 21(7), 1237–1245 (2005)

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially funded by the OMICAS program: Optimización Multiescala In-silico de Cultivos Agrícolas Sostenibles (Infraestructura y Validación en Arroz y Caña de Azúcar), anchored at the Pontificia Universidad Javeriana in Cali and funded within the Colombian Scientific Ecosystem by The World Bank, the Colombian Ministry of Science, Technology and Innovation, the Colombian Ministry of Education and the Colombian Ministry of Industry and Turism, and ICETEX, under GRANT ID: FP44842-217-2018. The second author was partially supported by Fundación CeiBA.

Author information

Authors and Affiliations

  1. Department of Electronics and Computer Science, Pontificia Universidad Javeriana, Cali, Colombia

    Miguel Romero, Óscar Ramírez, Jorge Finke & Camilo Rocha

Authors
  1. Miguel Romero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Óscar Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jorge Finke
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Camilo Rocha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miguel Romero .

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas Universidad Politécnica de Madrid, Universidad Politécnica de Madrid, Madrid, Madrid, Spain

    Rosa Maria Benito

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I, UFR Sciences et Techniques, University of Burgundy, Dijon, France

    Hocine Cherifi

  4. Grupo Interdisciplinar de Sistemas Compl, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Center for Complex Networks and Systems Research School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

  6. Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Tarragona, Spain

    Marta Sales-Pardo

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Romero, M., Ramírez, Ó., Finke, J., Rocha, C. (2022). Supervised Gene Function Prediction Using Spectral Clustering on Gene Co-expression Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-93413-2_54

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93412-5

  • Online ISBN: 978-3-030-93413-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation