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funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model

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Title funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model
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Creator Prabina Kumar Meher
Tanmaya Kumar Sahu
Shachi Gahoi
Ruchi Tomar
Atmakuri Ramakrishna Rao
 
Subject BOLD systems
CBOL
DNA barcode
Fungal taxonomy
ITS
 
Description Not Available
Identification of unknown fungal species aids to the conservation of fungal diversity. As many fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For fungal taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of fungal species is challenging due to high degree of variability among ITS regions within species.
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Date 2022-08-07T06:08:25Z
2022-08-07T06:08:25Z
2019-01-07
 
Type Research Paper
 
Identifier Meher, P.K., Sahu, T.K., Gahoi, S. et al. (2019). funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model. BMC Genet 20, 2 . https://doi.org/10.1186/s12863-018-0710-z
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http://krishi.icar.gov.in/jspui/handle/123456789/73730
 
Language English
 
Relation Not Available;
 
Publisher Not Available