ArticleBioinformation2018
Current Bioinformatics resources in combating infectious diseases.
Article in Bioinformation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed, 29 citations in OpenAlex.
- Leptospirosis in India: insights on circulating serovars, research lacunae and proposed strategies to control through one health approach.One health outlook · 2024Review
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- Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data.Frontiers in digital health · 2024Review
- Article
- PDAUG: a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling.BMC bioinformatics · 2022Article
- Teaching next-generation sequencing to medical students with a portable sequencing device.Perspectives on medical education · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bioinformatics tools and techniques analyzing next-generation sequencing (NGS) data are increasingly used for the diagnosis and monitoring of infectious diseases. It is of interest to review the application of bioinformatics tools, commonly used databases and NGS data in clinical microbiology, focusing on molecular identification, genotypic, microbiome research, antimicrobial resistance analysis and detection of unknown disease-associated pathogens in clinical specimens. This review documents available bioinformatics resources and databases that are used by medical microbiology scientists and physicians to control emerging infectious pathogens.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.