ArticleInterdisciplinary sciences, computational life sciences2019
CNN-MGP: Convolutional Neural Networks for Metagenomics Gene Prediction.
Article in Interdisciplinary sciences, computational life sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Advances in Decoding Bacterial N-Terminal Proteoforms: Technologies, Challenges, and Functional Insights.Microorganisms · 2026Review
- Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment response.Nature communications · 2026Article
- The hidden players: LncRNA-Encoded micropeptides in cancer hallmarks.Cellular and molecular life sciences : CMLS · 2026Review
- Multi-feature fusion for gene prediction and functional peptide identification.Frontiers in microbiology · 2026Article
- Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.iScience · 2025Review
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- Genomic language models (gLMs) decode bacterial genomes for improved gene prediction and translation initiation site identification.Briefings in bioinformatics · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- Analysis of metagenomic data.Nature reviews. Methods primers · 2025Article
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- Design of Interoperable Electronic Health Record (EHR) Application for Early Detection of Lung Diseases Using a Decision Support System by Expanding Deep Learning Techniques.The open respiratory medicine journal · 2024Article
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- Investigation of machine learning algorithms for taxonomic classification of marine metagenomes.Microbiology spectrum · 2023Article
- Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health.Plants (Basel, Switzerland) · 2023Review
- A toolbox of machine learning software to support microbiome analysis.Frontiers in microbiology · 2023Review
- Development and performance evaluation of an artificial intelligence algorithm using cell-free DNA fragment distance for non-invasive prenatal testing (aiD-NIPT).Frontiers in genetics · 2022Article
- Genomics enters the deep learning era.PeerJ · 2022Review
- WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs.PloS one · 2022Article
- New methodology for repetitive sequences identification inBiomedical signal processing and control · 2021Article
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2 authors.
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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 exclusive datasets based on pre-defined GC content ranges. We extract ORFs from each fragment; then, the ORFs are encoded numerically and inputted into an appropriate CNN model based on the fragment-GC content. The output from the CNN is the probability that an ORF will encode a gene. Finally, a greedy algorithm is used to select the final gene list. Overall, CNN-MGP is effective and achieves a 91% accuracy on testing dataset. CNN-MGP shows the ability of deep learning to predict genes in metagenomics fragments, and it achieves an accuracy higher than or comparable to state-of-the-art gene-prediction programs that use pre-defined features.
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