Análise de composição de conjunto de treinamento para avaliação de aprendizagem de máquina aplicada à predição de genes

Silva, Raíssa Da; Padovani, Kleber; Santos, Wendel; Xavier, Roberto Perez; Alves, Ronnie

doi:10.5753/bsb_estendido.2018.8798

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Initially, 20 complete genomes were used to tune parameters that contributed to the differentiation of gene and intergenic region, as well as to identify the characteristics of sequences useful to generate the learning model. This model was then validated on 5 different genomes of the training set introduced in [17]. Next, a more enriched model was built, consisting of 129 complete genomes and their respective annotations, of which 11 are archaeas and 118 are bacteria.…”

Section: Training and Validation Datasetsmentioning

confidence: 99%

geneRFinder: gene finding in distinct metagenomic data complexities

Silva

Padovani

Góes

et al. 2020

Preprint

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MotivationMicrobes perform a fundamental economic, social and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the functions they play in those environments and their responsibility is measured by their genes. The advances of next-generation sequencing technology have facilitated metagenomics research however it also create a heavy computational burden. Large and complex biological datasets are available as never before. There are many gene predictors available which can aid gene annotation process though they lack of handling appropriately metagenomic data complexities. There is no standard metagenomic benchmark data for gene prediction. Thus, gene predictors may inflate their results by obfuscating low false discovery rates.ResultsWe introduce geneRFinder, a ML-based gene predictor able to outperform state-of-the-art gene prediction tools across this benchmark by using only one pre-trained Random Forest model. Average prediction rates of geneRFinder differed in percentage terms by 54% and 64%, respectively, against Prodigal and FragGeneScan while handling high complexity metagenomes. The specificity rate of geneRFinder had the largest distance against FragGeneScan, 79 percentage points, and 66 more than Prodigal. According to McNemar’s test, all percentual differences between predictors performances are statistically significant for all datasets with a 99% confidence interval.ConclusionsWe provide geneRFinder, a approach for gene prediction in distinct metagenomic complexities, available at github.com/railorena/geneRFinder, and also we provide a novel, comprehensive benchmark data for gene prediction — which is based on The Critical Assessment of Metagenome Interpretation (CAMI) challenge, and contains labeled data from gene regions – avaliable at sourceforge.net/p/generfinder-benchmark.

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Section: Training and Validation Datasetsmentioning

confidence: 99%

geneRFinder: gene finding in distinct metagenomic data complexities

Silva

Padovani

Góes

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

geneRFinder: gene finding in distinct metagenomic data complexities

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background Microbes perform a fundamental economic, social, and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the functions they play in those environments and their responsibility is measured by their genes. The advances of next-generation sequencing technology have facilitated metagenomics research however it also creates a heavy computational burden. Large and complex biological datasets are available as never before. There are many gene predictors available that can aid the gene annotation process though they lack handling appropriately metagenomic data complexities. There is no standard metagenomic benchmark data for gene prediction. Thus, gene predictors may inflate their results by obfuscating low false discovery rates. Results We introduce geneRFinder, an ML-based gene predictor able to outperform state-of-the-art gene prediction tools across this benchmark by using only one pre-trained Random Forest model. Average prediction rates of geneRFinder differed in percentage terms by 54% and 64%, respectively, against Prodigal and FragGeneScan while handling high complexity metagenomes. The specificity rate of geneRFinder had the largest distance against FragGeneScan, 79 percentage points, and 66 more than Prodigal. According to McNemar’s test, all percentual differences between predictors performances are statistically significant for all datasets with a 99% confidence interval. Conclusions We provide geneRFinder, an approach for gene prediction in distinct metagenomic complexities, available at gitlab.com/r.lorenna/generfinder and https://osf.io/w2yd6/, and also we provide a novel, comprehensive benchmark data for gene prediction—which is based on The Critical Assessment of Metagenome Interpretation (CAMI) challenge, and contains labeled data from gene regions—available at https://sourceforge.net/p/generfinder-benchmark.

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Análise de composição de conjunto de treinamento para avaliação de aprendizagem de máquina aplicada à predição de genes

Cited by 2 publications

References 8 publications

geneRFinder: gene finding in distinct metagenomic data complexities

geneRFinder: gene finding in distinct metagenomic data complexities

geneRFinder: gene finding in distinct metagenomic data complexities

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