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Bioinformation. 2019 Jan 31;15(1):26-32. doi: 10.6026/97320630015026. eCollection 2019.

Classification of Functional Metagenomes Recovered from Different Environmental Samples.

Author information

1
Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi-6205,Bangladesh.
2
Institute of Environmental Science,University of Rajshahi-6205,Bangladesh.
3
Agricultural Statistics and Information and Communication Technology (ASICT) Division,Bangladesh Agricultural Research Institute(BARI),Joydebpur,Gazipur-1701,Bangladesh.
4
Bangabandhu Sheikh Mujibur Rahaman Agricultural University,Joydebpur,Gazipur-1706, Bangladesh.
5
Emerging Infections, Infectious Diseases Division,International Centre for Diarrheal Disease Research,Bangladesh (icddr,b).

Abstract

Classification of functional metagenomes from the microbial community plays the vital role in the metagenomics research. In this paper, an investigation was made to study the performance of beta-t random forest classifier for classification of metagenomics data. Nine key functional meta-genomic variables were selected using the beta-t test statistic from the 10 different microbial community using p-value at 5% level of significance. Then beta-t random forest classifier showed the higher accuracy (96%), true positive rate (96%) and lower false positive rate (5%), false discovery rate (5%) and misclassification error rate (5%) for classification of metagenomes. This method showed the better performance compare to Bayes, SVM, KNN, AdaBoost and LogitBoost).

KEYWORDS:

beta-t random forest; classification; false positive rate; metagenomes; misclassification error; true positive rate

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