Use este identificador para citar ou linkar para este item: https://locus.ufv.br//handle/123456789/12748
Tipo: Artigo
Título: Geminivirus data warehouse: a database enriched with machine learning approaches
Autor(es): Silva, Jose Cleydson F.
Carvalho, Thales F. M.
Basso, Marcos F.
Deguchi, Michihito
Pereira, Welison A.
Vidigal, Pedro M. P.
Brustolini, Otávio J. B.
Silva, Fabyano F.
Dal-Bianco, Maximiller
Fontes, Renildes L. F.
Santos, Anésia A.
Zerbini, Francisco Murilo
Cerqueira, Fabio R.
Fontes, Elizabeth P. B.
R. Sobrinho, Roberto
Abstract: The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic losses worldwide. Geminiviruses are divided into nine genera, according to their insect vector, host range, genome organization, and phylogeny reconstruction. Using rolling-circle amplification approaches along with high-throughput sequencing technologies, thousands of full-length geminivirus and satellite genome sequences were amplified and have become available in public databases. As a consequence, many important challenges have emerged, namely, how to classify, store, and analyze massive datasets as well as how to extract information or new knowledge. Data mining approaches, mainly supported by machine learning (ML) techniques, are a natural means for high-throughput data analysis in the context of genomics, transcriptomics, proteomics, and metabolomics. Here, we describe the development of a data warehouse enriched with ML approaches, designated geminivirus.org. We implemented search modules, bioinformatics tools, and ML methods to retrieve high precision information, demarcate species, and create classifiers for genera and open reading frames (ORFs) of geminivirus genomes. The use of data mining techniques such as ETL (Extract, Transform, Load) to feed our database, as well as algorithms based on machine learning for knowledge extraction, allowed us to obtain a database with quality data and suitable tools for bioinformatics analysis. The Geminivirus Data Warehouse (geminivirus.org) offers a simple and user-friendly environment for information retrieval and knowledge discovery related to geminiviruses.
Palavras-chave: Machine learning
Knowledge discovery
Data mining
Geminivirus
Data warehouse
Random forest
Editor: BioMed Central Bioinformatics
Tipo de Acesso: Open Access
URI: https://doi.org/10.1186/s12859-017-1646-4
http://www.locus.ufv.br/handle/123456789/12748
Data do documento: 5-Mai-2017
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