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In-silico Gene Annotation Prediction Using the Co-expression Network Structure

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 882))

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Abstract

Identifying which genes are involved in particular biological processes is relevant to understand the structure and function of a genome. A number of techniques have been proposed that aim to annotate genes, i.e., identify unknown biological associations between biological processes and genes. The ultimate goal of these techniques is to narrow down the search for promising candidates to carry out further studies through in-vivo experiments. This paper presents an approach for the in-silico prediction of functional gene annotations. It uses existing knowledge body of gene annotations of a given genome and the topological properties of its gene co-expression network, to train a supervised machine learning model that is designed to discover unknown annotations. The approach is applied to Oryza Sativa Japonica (a variety of rice). Our results show that the topological properties help in obtaining a more precise prediction for annotating genes.

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Acknowledgments

The authors would like to thank the anonymous referees for their helpful comments. This work was funded by the OMICAS program: Optimización Multiescala In-silico de Cultivos Agrícolas Sostenibles (Infraestructura y Validación en Arroz y Caña de Azúcar), sponsored within the Colombian Scientific Ecosystem by The World Bank, Colciencias, Icetex, the Colombian Ministry of Education and the Colombian Ministry of Industry and Turism under Grant FP44842-217-2018.

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Authors and Affiliations

  1. Pontificia Universidad Javeriana Cali, Cali, Colombia

    Miguel Romero, Jorge Finke, Mauricio Quimbaya & Camilo Rocha

Authors
  1. Miguel Romero
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  2. Jorge Finke
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  3. Mauricio Quimbaya
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  4. Camilo Rocha
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Corresponding author

Correspondence to Miguel Romero .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Romero, M., Finke, J., Quimbaya, M., Rocha, C. (2020). In-silico Gene Annotation Prediction Using the Co-expression Network Structure. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_64

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