funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model
KRISHI: Publication and Data Inventory Repository
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Title |
funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model
Not Available |
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Creator |
Prabina Kumar Meher
Tanmaya Kumar Sahu Shachi Gahoi Ruchi Tomar Atmakuri Ramakrishna Rao |
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Subject |
BOLD systems
CBOL DNA barcode Fungal taxonomy ITS |
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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. Not Available |
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Date |
2022-08-07T06:08:25Z
2022-08-07T06:08:25Z 2019-01-07 |
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Type |
Research Paper
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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
Not Available http://krishi.icar.gov.in/jspui/handle/123456789/73730 |
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Language |
English
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Relation |
Not Available;
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Publisher |
Not Available
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