Classification of Functional Metagenomes Recovered from Different Environmental Samples.

Zobaer Akond, Mohammad Nazmol Hasan, Md Jahangir Alam, Munirul Alam, Md Nurul Haque Mollah

Abstract read

In one paragraph

Article in Bioinformation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Machine learning framework for gut microbiome biomarkers discovery and modulation analysis in large-scale obese population.BMC genomics · 2022
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zobaer AkondBioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi-6205,Bangladesh.

Mohammad Nazmol HasanBioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi-6205,Bangladesh.

Md Jahangir AlamBioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi-6205,Bangladesh.

Munirul AlamEmerging Infections, Infectious Diseases Division,International Centre for Diarrheal Disease Research,Bangladesh (icddr,b).

Md Nurul Haque MollahBangabandhu Sheikh Mujibur Rahaman Agricultural University,Joydebpur,Gazipur-1706, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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).

Indexed as

beta-t random forestclassificationfalse positive ratemetagenomesmisclassification errortrue positive rate

Identifiers

PMID31359995

DOI10.6026/97320630015026

PMCPMC6651027

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.