An analysis of non-cultivable bacteria using WEKA.

Pritee Chunarkar Patil, Pradnya Suresh Panchal, Shweta Madiwale, Vidya Sunil Tale

Abstract read

In one paragraph

Article in Bioinformation, 2020. 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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System?International journal of environmental research and public health · 2021
Article

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Pritee Chunarkar PatilDepartment of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth Deemed University, Pune, Maharashtra, India.

Pradnya Suresh PanchalDepartment of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth Deemed University, Pune, Maharashtra, India.

Shweta MadiwaleDepartment of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth Deemed University, Pune, Maharashtra, India.

Vidya Sunil TaleDepartment of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth Deemed University, Pune, Maharashtra, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study of metagenomics from high throughput sequencing data processed through Waikato Environment for Knowledge Analysis (WEKA) is gaining momentum in recent years. Therefore, we report an analysis of metagenome data generated using T-RFLP followed by using the SMO (Sequential minimal optimization) algorithm in WEKA to identify the total amount of cultured and uncultured microorganism present in the sample collected from multiple sources.

Indexed as

MetagenomicsSMO algorithmT-RFLPUncultured microorganismsWEKA

Identifiers

PMID33214750

DOI10.6026/97320630016620

PMCPMC7649025

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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.