ArticleFrontiers in microbiology2012
Genome-Wide Scale-Free Network Inference for Candida albicans.
Article in Frontiers in microbiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 27 citations in OpenAlex.
- A comprehensive transcription factor and DNA-binding motif resource for the construction of gene regulatory networks inComputational and structural biotechnology journal · 2021Article
- ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks.Scientific reports · 2018Article
- Elucidating the Interaction of CF Airway Epithelial Cells and Rhinovirus: Using the Host-Pathogen Relationship to Identify Future Therapeutic Strategies.Frontiers in pharmacology · 2018Review
- Systems Biology Approaches for Host-Fungal Interactions: An Expanding Multi-Omics Frontier.Omics : a journal of integrative biology · 2016Review
- Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens.Frontiers in microbiology · 2016Article
- How to Predict Molecular Interactions between Species?Frontiers in microbiology · 2016Review
- Network-assisted genetic dissection of pathogenicity and drug resistance in the opportunistic human pathogenic fungus Cryptococcus neoformans.Scientific reports · 2015Article
- A review on computational systems biology of pathogen-host interactions.Frontiers in microbiology · 2015Review
- Computational prediction of molecular pathogen-host interactions based on dual transcriptome data.Frontiers in microbiology · 2015Article
- Data- and knowledge-based modeling of gene regulatory networks: an update.EXCLI journal · 2015Review
- Construction and verification of the transcriptional regulatory response network of Streptococcus mutans upon treatment with the biofilm inhibitor carolacton.BMC genomics · 2014Article
- Interactive exploration of integrated biological datasets using context-sensitive workflows.Frontiers in genetics · 2014Article
- Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0.BMC systems biology · 2013Article
- The extended TILAR approach: a novel tool for dynamic modeling of the transcription factor network regulating the adaption to in vitro cultivation of murine hepatocytes.BMC systems biology · 2012Article
- Systems biology of host-fungus interactions: turning complexity into simplicity.Current opinion in microbiology · 2012Review
- An Interspecies Regulatory Network Inferred from Simultaneous RNA-seq of Candida albicans Invading Innate Immune Cells.Frontiers in microbiology · 2012Article
- Systems biology of microbial infection.Frontiers in microbiology · 2012Article
- Systems biology of fungal infection.Frontiers in microbiology · 2012Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Discovery of essential genes in pathogenic organisms is an important step in the development of new medication. Despite a growing number of genome data available, little is known about C. albicans, a major fungal pathogen. Most of the human population carries C. albicans as commensal, but it can cause systemic infection that may lead to the death of the host if the immune system has deteriorated. In many organisms central nodes in the interaction network (hubs) play a crucial role for information and energy transport. Knock-outs of such hubs often lead to lethal phenotypes making them interesting drug targets. To identify these central genes via topological analysis, we inferred gene regulatory networks that are sparse and scale-free. We collected information from various sources to complement the limited expression data available. We utilized a linear regression algorithm to infer genome-wide gene regulatory interaction networks. To evaluate the predictive power of our approach, we used an automated text-mining system that scanned full-text research papers for known interactions. With the help of the compendium of known interactions, we also optimize the influence of the prior knowledge and the sparseness of the model to achieve the best results. We compare the results of our approach with those of other state-of-the-art network inference methods and show that we outperform those methods. Finally we identify a number of hubs in the genome of the fungus and investigate their biological relevance.
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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.