ArticleScientific reports2017
Benchmarking selected computational gene network growing tools in context of virus-host interactions.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Classifying Integrated Signature Molecules in Macrophages of Rheumatoid Arthritis, Osteoarthritis, and Periodontal Disease: An Omics-Based Study.Current issues in molecular biology · 2022Article
- Network Theoretical Approach to Explore Factors Affecting Signal Propagation and Stability in Dementia's Protein-Protein Interaction Network.Biomolecules · 2022Article
- Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis.Clinical and experimental pediatrics · 2021Article
- Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models.Frontiers in genetics · 2021Article
- A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery.BMC bioinformatics · 2019Article
- Article
- Inter-Species Host Gene Expression Differences in Response to Human and Avian Influenza A Virus Strains.International journal of molecular sciences · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several available online tools provide network growing functions where an algorithm utilizing different data sources suggests additional genes/proteins that should connect an input gene set into functionally meaningful networks. Using the well-studied system of influenza host interactions, we compare the network growing function of two free tools GeneMANIA and STRING and the commercial IPA for their performance of recovering known influenza A virus host factors previously identified from siRNA screens. The result showed that given small (~30 genes) or medium (~150 genes) input sets all three network growing tools detect significantly more known host factors than random human genes with STRING overall performing strongest. Extending the networks with all the three tools significantly improved the detection of GO biological processes of known host factors compared to not growing networks. Interestingly, the rate of identification of true host factors using computational network growing is equal or better to doing another experimental siRNA screening study which could also be true and applied to other biological pathways/processes.
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.