ArticleExperimental and therapeutic medicine2018
Identifying pathway modules of tuberculosis in children by analyzing multiple different networks.
Article in Experimental and therapeutic medicine, 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, 3 citations in OpenAlex.
- Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis.JCI insight · 2020Article
- DiffGRN: differential gene regulatory network analysis.International journal of data mining and bioinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB), which is caused by the mycobacterium TB, is the major cause of human death worldwide. The aim of this study was to identify the biomarkers involved in child TB. Gene expression data were obtained from the Array Express Archive of Functional Genomics Data. Gene expression data and protein-protein interaction (PPI) data were downloaded to construct differential gene co-expression networks (DCNs). The Benjamini-Hochberg algorithm was used to correct the P-value. In total, 3,820 edges (PPIs) and 1,359 nodes (genes) were obtained from the human-related PPIs data and gene expression data at the criteria of absolute value of Pearson's correlation coefficient >0.8. The DCNs were formed by these edges and nodes. Thirteen seed genes were obtained by ranging z-scores. Eight significant multiple different modules were identified from DCNs using the statistical significant test. In conclusion, the seed genes and significant modules constitute potential biomarkers that reveal the underlying mechanisms in child TB. The new identified biomarkers may contribute to an understanding of TB and provide a new therapeutic method for the treatment of TB.
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.