CNN-MGP: Convolutional Neural Networks for Metagenomics Gene Prediction.

ABSTRACT: Accurate gene prediction in metagenomics fragments is a computationally challenging task due to the short-read length, incomplete, and fragmented nature of the data. Most gene-prediction programs are based on extracting a large number of features and then applying statistical approaches or supervised classification approaches to predict genes. In our study, we introduce a convolutional neural network for metagenomics gene prediction (CNN-MGP) program that predicts genes in metagenomics fragments directly from raw DNA sequences, without the need for manual feature extraction and feature selection stages. CNN-MGP is able to learn the characteristics of coding and non-coding regions and distinguish coding and non-coding open reading frames (ORFs). We train 10 CNN models on 10 mutually exclusi

SUBMITTER: Al-Ajlan A 

PROVIDER: S-EPMC6841655 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature