ArticleCurrent protocols in bioinformatics2018
Leveraging Experimental Details for an Improved Understanding of Host-Pathogen Interactome.
Article in Current protocols in bioinformatics, 2018. 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
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
2 citing papers in PubMed.
- The Intricacy of the Viral-Human Protein Interaction Networks: Resources, Data, and Analyses.Frontiers in microbiology · 2022Review
- Towards a unified open access dataset of molecular interactions.Nature communications · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
An increasing proportion of curated host-pathogen interaction (HPI) information is becoming available in interaction databases. These data represent detailed, experimentally-verified, molecular interaction data, which may be used to better understand infectious diseases. By their very nature, HPIs are context dependent, where the outcome of two proteins as interacting or not depends on the precise biological conditions studied and approaches used for identifying these interactions. The associated biology and the technical details of the experiments identifying interacting protein molecules are increasing being curated using defined curation standards but are overlooked in current HPI network modeling. Given the increase in data size and complexity, awareness of the process and variables included in HPI identification and curation, and their effect on data analysis and interpretation is crucial in understanding pathogenesis. We describe the use of HPI data for network modeling, aspects of curation that can help researchers to more accurately model specific infection conditions, and provide examples to illustrate these principles. © 2018 by John Wiley & Sons, Inc.
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Registered trials
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