ReviewComputational and structural biotechnology journal2014
Using biological networks to improve our understanding of infectious diseases.
Review in Computational and structural biotechnology journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 57 citations in OpenAlex.
- Article
- Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare.Briefings in bioinformatics · 2024Review
- Protein network analysis and functional enrichment via computational biotechnology unravel molecular and pathogenic mechanisms of kidney stone disease.Biomedical journal · 2023Review
- Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective.Frontiers in genetics · 2023Article
- Synergetic antibacterial activity ofMetabolism open · 2022Article
- Review
- Network-driven analysis of human-Plasmodium falciparum interactome: processes for malaria drug discovery and extracting in silico targets.Malaria journal · 2021Article
- k-core genes underpin structural features of breast cancer.Scientific reports · 2021Article
- IHP-PING-generating integrated human protein-protein interaction networks on-the-fly.Briefings in bioinformatics · 2021Article
- Pairwise Versus Multiple Global Network Alignment.IEEE access : practical innovations, open solutions · 2020Article
- Post genome-wide association analysis: dissecting computational pathway/network-based approaches.Briefings in bioinformatics · 2019Review
- Prediction of cassava protein interactome based on interolog method.Scientific reports · 2017Article
- A post-gene silencing bioinformatics protocol for plant-defence gene validation and underlying process identification: case study of the Arabidopsis thaliana NPR1.BMC plant biology · 2017Article
- The effects of shared information on semantic calculations in the gene ontology.Computational and structural biotechnology journal · 2017Article
- Systems biology approach: Panacea for unravelling host-virus interactions and dynamics of vaccine induced immune response.Gene reports · 2016Review
- In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks.BMC systems biology · 2016Article
- Evaluation of the in vitro antibacterial activity of the solvent fractions of the leaves of Rhamnus prinoides L'Herit (Rhamnaceae) against pathogenic bacteria.BMC complementary and alternative medicine · 2016Article
- A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein-Protein Interaction Networks: A Case Study of Mycobacterium leprae.Frontiers in genetics · 2016Article
- The post-genomic era of biological network alignment.EURASIP journal on bioinformatics & systems biology · 2015Article
- A review on computational systems biology of pathogen-host interactions.Frontiers in microbiology · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating the most common infectious diseases, in many cases the mechanism of action of these drugs or even their targets in the pathogen remain unknown. In addition, the key factors or processes in pathogens that facilitate infection and disease progression are often not well understood. Since proteins do not work in isolation, understanding biological systems requires a better understanding of the interconnectivity between proteins in different pathways and processes, which includes both physical and other functional interactions. Such biological networks can be generated within organisms or between organisms sharing a common environment using experimental data and computational predictions. Though different data sources provide different levels of accuracy, confidence in interactions can be measured using interaction scores. Connections between interacting proteins in biological networks can be represented as graphs and edges, and thus studied using existing algorithms and tools from graph theory. There are many different applications of biological networks, and here we discuss three such applications, specifically applied to the infectious disease tuberculosis, with its causative agent Mycobacterium tuberculosis and host, Homo sapiens. The applications include the use of the networks for function prediction, comparison of networks for evolutionary studies, and the generation and use of host-pathogen interaction networks.
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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.