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Classification of Functional Metagenomes Recovered from Different Environmental Samples



Zobaer Akond1, 2, 3,*, Mohammad Nazmol Hasan1,5, Md. Jahangir Alam1, Munirul Alam4, Md. Nurul Haque Mollah5



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



Zobaer Akond; *Corresponding Author


Article Type

Research Article



Received December 13, 2018; Accepted December 26, 2018; Published January 31, 2019



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 & LogitBoost).



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



Received December 13, 2018; Accepted December 26, 2018; Published January 31, 2019


Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.