ArticleGenes2023
CNN_FunBar: Advanced Learning Technique for Fungi ITS Region Classification.
Article in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Ability to use ITS and rbcL sequencing for determination of the genetic diversity and relationships among olive (3 Biotech · 2025Article
- Fungal taxonomy: A puzzle with many missing piecesBiomedica : revista del Instituto Nacional de Salud · 2023Article
- Ensemble learning-based approach for automatic classification of termite mushrooms.Frontiers in genetics · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fungal species identification from metagenomic data is a highly challenging task. Internal Transcribed Spacer (ITS) region is a potential DNA marker for fungi taxonomy prediction. Computational approaches, especially deep learning algorithms, are highly efficient for better pattern recognition and classification of large datasets compared to in silico techniques such as BLAST and machine learning methods. Here in this study, we present CNN_FunBar, a convolutional neural network-based approach for the classification of fungi ITS sequences from UNITE+INSDC reference datasets. Effects of convolution kernel size, filter numbers,
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