ArticleBMC systems biology2010
Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets.
Article in BMC systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 35 citations in OpenAlex.
- LncRNA-mRNA co-expression network revealing the regulatory roles of lncRNAs in melanogenesis in vitiligo.Journal of human genetics · 2022Article
- EpiGraph: an open-source platform to quantify epithelial organization.Bioinformatics (Oxford, England) · 2020Article
- Distinctive Behaviors of Druggable Proteins in Cellular Networks.PLoS computational biology · 2015Article
- Exploring the structure and function of temporal networks with dynamic graphlets.Bioinformatics (Oxford, England) · 2015Article
- A molecular systems approach to modelling human skin pigmentation: identifying underlying pathways and critical components.BMC research notes · 2015Article
- Predicting disease associations via biological network analysis.BMC bioinformatics · 2014Article
- Estimating the activity of transcription factors by the effect on their target genes.Bioinformatics (Oxford, England) · 2014Article
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- Identification of human disease genes from interactome network using graphlet interaction.PloS one · 2014Article
- Survey of network-based approaches to research of cardiovascular diseases.BioMed research international · 2014Review
- Network topology reveals key cardiovascular disease genes.PloS one · 2013Article
- Biological function through network topology: a survey of the human diseasome.Briefings in functional genomics · 2012Article
- Graphlet-based edge clustering reveals pathogen-interacting proteins.Bioinformatics (Oxford, England) · 2012Article
- A systems biology approach to the global analysis of transcription factors in colorectal cancer.BMC cancer · 2012Article
- OPRM1 and EGFR contribute to skin pigmentation differences between Indigenous Americans and Europeans.Human genetics · 2012Article
- Bottlenecks caused by software gaps in miRNA and RNAi research.Pharmaceutical research · 2012Article
- GraphCrunch 2: Software tool for network modeling, alignment and clustering.BMC bioinformatics · 2011Article
- Dominating biological networks.PloS one · 2011Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundRNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis.
resultsIn this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes.
conclusionsWe present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches.
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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.