ArticleBacteriophage
Bioinformatics as a first-line approach for understanding bacteriophage transcription.
Article in Bacteriophage. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- iProm-phage: A two-layer model to identify phage promoters and their types using a convolutional neural network.Frontiers in microbiology · 2022Article
- Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current approach to understanding bacteriophage transcription strategies during infection includes a combination of experimental and bioinformatics approaches, which is often time and resource consuming. Given the exponentially growing number of sequenced bacteriophage genomes, it becomes sensible asking to what extent one can understand bacteriophage transcription by using bioinformatics methods alone. We here argue that a suitable choice of computational methods may provide a highly efficient first-line approach for underst-anding bacteriophage transcription.
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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.