ArticleScientific reports2020
Convolutional neural networks improve fungal classification.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Advancing Yeast Identification Using High-Throughput DNA Barcode Data From a Curated Culture Collection.Molecular ecology resources · 2026Article
- Phytomycobiomes and Ecosystem Services: Mechanisms, Evidence and Routes to Application.Journal of fungi (Basel, Switzerland) · 2025Review
- HFTC: a hierarchical fungal taxonomic classification model for ITS sequences using low-dimensional embedding features.Frontiers in genetics · 2025Article
- Deep learning-based characterization of neutrophil activation phenotypes inComputational and structural biotechnology journal · 2024Article
- iDeLUCS: a deep learning interactive tool for alignment-free clustering of DNA sequences.Bioinformatics (Oxford, England) · 2023Article
- Article
- Dnabarcoder: An open-source software package for analysing and predicting DNA sequence similarity cutoffs for fungal sequence identification.Molecular ecology resources · 2022Article
- Species determination using AI machine-learning algorithms: Hebeloma as a case study.IMA fungus · 2022Article
- Improving Taxonomic Delimitation of Fungal Species in the Age of Genomics and Phenomics.Frontiers in microbiology · 2022Review
- Fungal taxonomy and sequence-based nomenclature.Nature microbiology · 2021Review
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sequence classification plays an important role in metagenomics studies. We assess the deep neural network approach for fungal sequence classification as it has emerged as a successful paradigm for big data classification and clustering. Two deep learning-based classifiers, a convolutional neural network (CNN) and a deep belief network (DBN) were trained using our recently released barcode datasets. Experimental results show that CNN outperformed the traditional BLAST classification and the most accurate machine learning based Ribosomal Database Project (RDP) classifier on datasets that had many of the labels present in the training datasets. When classifying an independent dataset namely the "Top 50 Most Wanted Fungi", CNN and DBN assigned less sequences than BLAST. However, they could assign much more sequences than the RDP classifier. In terms of efficiency, it took the machine learning classifiers up to two seconds to classify a test dataset while it was 53 s for BLAST. The result of the current study will enable us to speed up the taxonomic assignments for the fungal barcode sequences generated at our institute as ~ 70% of them still need to be validated for public release. In addition, it will help to quickly provide a taxonomic profile for metagenomics samples.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.