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Biomed Res Int. 2017;2017:4740354. doi: 10.1155/2017/4740354. Epub 2017 Nov 8.

Gene Prediction in Metagenomic Fragments with Deep Learning.

Author information

1
Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.

Abstract

Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.

PMID:
29250541
PMCID:
PMC5698827
DOI:
10.1155/2017/4740354
[Indexed for MEDLINE]
Free PMC Article

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