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

Miguel Romero; Jorge Finke; Mauricio Quimbaya; Camilo Rocha

doi:10.1007/978-3-030-36683-4_64

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

Miguel Romero, Jorge Finke, Mauricio Quimbaya, Camilo Rocha

Universidad Javeriana

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019
Editors	Hocine Cherifi, Sabrina Gaito, José Fernendo Mendes, Esteban Moro, Luis Mateus Rocha
Publisher	Springer
Pages	802-812
Number of pages	11
ISBN (Print)	9783030366827
DOIs	https://doi.org/10.1007/978-3-030-36683-4_64
State	Published - 2020
Event	8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019 - Lisbon, Portugal Duration: 10 Dec 2019 → 12 Dec 2019

Publication series

Name	Studies in Computational Intelligence
Volume	882 SCI
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Conference

Conference	8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019
Country/Territory	Portugal
City	Lisbon
Period	10/12/19 → 12/12/19

Keywords

Co-expression network
Functional gene annotation
Machine learning
Oryza Sativa Japonica
Topological properties

Access to Document

10.1007/978-3-030-36683-4_64

Cite this

Romero, M., Finke, J., Quimbaya, M., & Rocha, C. (2020). In-silico Gene Annotation Prediction Using the Co-expression Network Structure. In H. Cherifi, S. Gaito, J. F. Mendes, E. Moro, & L. M. Rocha (Eds.), Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019 (pp. 802-812). (Studies in Computational Intelligence; Vol. 882 SCI). Springer. https://doi.org/10.1007/978-3-030-36683-4_64

Romero, Miguel ; Finke, Jorge ; Quimbaya, Mauricio et al. / In-silico Gene Annotation Prediction Using the Co-expression Network Structure. Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019. editor / Hocine Cherifi ; Sabrina Gaito ; José Fernendo Mendes ; Esteban Moro ; Luis Mateus Rocha. Springer, 2020. pp. 802-812 (Studies in Computational Intelligence).

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title = "In-silico Gene Annotation Prediction Using the Co-expression Network Structure",

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.",

keywords = "Co-expression network, Functional gene annotation, Machine learning, Oryza Sativa Japonica, Topological properties",

author = "Miguel Romero and Jorge Finke and Mauricio Quimbaya and Camilo Rocha",

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language = "English",

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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 H Cherifi, S Gaito, JF Mendes, E Moro & LM Rocha (eds), Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol. 882 SCI, Springer, pp. 802-812, 8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019, Lisbon, Portugal, 10/12/19. https://doi.org/10.1007/978-3-030-36683-4_64

In-silico Gene Annotation Prediction Using the Co-expression Network Structure. / Romero, Miguel; Finke, Jorge; Quimbaya, Mauricio et al.
Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019. ed. / Hocine Cherifi; Sabrina Gaito; José Fernendo Mendes; Esteban Moro; Luis Mateus Rocha. Springer, 2020. p. 802-812 (Studies in Computational Intelligence; Vol. 882 SCI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - In-silico Gene Annotation Prediction Using the Co-expression Network Structure

AU - Romero, Miguel

AU - Finke, Jorge

AU - Quimbaya, Mauricio

AU - Rocha, Camilo

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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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BT - Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019

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A2 - Gaito, Sabrina

A2 - Mendes, José Fernendo

A2 - Moro, Esteban

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PB - Springer

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Y2 - 10 December 2019 through 12 December 2019

ER -

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 JF, Moro E, Rocha LM, editors, Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019. Springer. 2020. p. 802-812. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-36683-4_64

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

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Keywords

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