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Title

Classification of Functional Metagenomes Recovered from Different Environmental Samples

 

Authors

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

 

Affiliation

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.

 

Email

Zobaer Akond E-mail:akond25@yahoo.com; *Corresponding Author

 

Article Type

Research Article

 

Date

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

 

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 & LogitBoost).

 

Keywords

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

 

Citation

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

 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

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.