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.

Idioma originalInglés
Título de la publicación alojadaComplex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019
EditoresHocine Cherifi, Sabrina Gaito, José Fernendo Mendes, Esteban Moro, Luis Mateus Rocha
Número de páginas11
ISBN (versión impresa)9783030366827
EstadoPublicada - 2020
Evento8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019 - Lisbon, Portugal
Duración: 10 dic. 201912 dic. 2019

Serie de la publicación

NombreStudies in Computational Intelligence
Volumen882 SCI
ISSN (versión impresa)1860-949X
ISSN (versión digital)1860-9503


Conferencia8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019


Profundice en los temas de investigación de 'In-silico Gene Annotation Prediction Using the Co-expression Network Structure'. En conjunto forman una huella única.

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