PyBact: an algorithm for bacterial identification.

Chanin Nantasenamat, Likit Preeyanon, Chartchalerm Isarankura-Na-Ayudhya, Virapong Prachayasittikul

Abstract read

In one paragraph

Article in EXCLI journal, 2011. 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.

Key messages from Experimental and Clinical Sciences.EXCLI journal · 2012
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

4 authors.

Chanin NantasenamatCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.

Likit PreeyanonCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.

Chartchalerm Isarankura-Na-AyudhyaDepartment of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.

Virapong PrachayasittikulDepartment of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PyBact is a software written in Python for bacterial identification. The code simulates the predefined behavior of bacterial species by generating a simulated data set based on the frequency table of biochemical tests from diagnostic microbiology textbook. The generated data was used for predictive model construction by machine learning approaches and results indicated that the classifiers could accurately predict its respective bacterial class with accuracy in excess of 99 %.

Indexed as

bacteriabacterial identificationdata miningmicrobiologyPyBactPython

Identifiers

PMID27857678

PMCPMC5109015

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.