TY - GEN
T1 - In-silico Gene Annotation Prediction Using the Co-expression Network Structure
AU - Romero, Miguel
AU - Finke, Jorge
AU - Quimbaya, Mauricio
AU - Rocha, Camilo
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
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.
KW - Co-expression network
KW - Functional gene annotation
KW - Machine learning
KW - Oryza Sativa Japonica
KW - Topological properties
UR - http://www.scopus.com/inward/record.url?scp=85085088046&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-36683-4_64
DO - 10.1007/978-3-030-36683-4_64
M3 - Conference contribution
AN - SCOPUS:85085088046
SN - 9783030366827
T3 - Studies in Computational Intelligence
SP - 802
EP - 812
BT - Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019
A2 - Cherifi, Hocine
A2 - Gaito, Sabrina
A2 - Mendes, José Fernendo
A2 - Moro, Esteban
A2 - Rocha, Luis Mateus
PB - Springer
T2 - 8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019
Y2 - 10 December 2019 through 12 December 2019
ER -