ArticleFrontiers in genetics2014
Utility of network integrity methods in therapeutic target identification.
Article in Frontiers in genetics, 2014. 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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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.
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Who cites it
18 citing papers in PubMed, 38 citations in OpenAlex.
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- Network analyses predict major regulators of resistance to early blight disease complex in tomato.BMC plant biology · 2024Article
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- In silico Methods for Identification of Potential Therapeutic Targets.Interdisciplinary sciences, computational life sciences · 2022Review
- Candesartan prevents arteriopathy progression in cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy model.The Journal of clinical investigation · 2021Article
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- Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks.Frontiers in genetics · 2020Article
- Centrality Analysis of Protein-Protein Interaction Networks and Molecular Docking Prioritize Potential Drug-Targets in Type 1 Diabetes.Iranian journal of pharmaceutical research : IJPR · 2020Article
- Analyzing the regulation of miRNAs on protein-protein interaction network in Hodgkin lymphoma.BMC bioinformatics · 2019Article
- Natural Product Target Network Reveals Potential for Cancer Combination Therapies.Frontiers in pharmacology · 2019Article
- Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine.Frontiers in genetics · 2019Article
- An integrative approach for building personalized gene regulatory networks for precision medicine.Genome medicine · 2018Review
- Reversal of dendritic phenotypes in 16p11.2 microduplication mouse model neurons by pharmacological targeting of a network hub.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Drug target prioritization by perturbed gene expression and network information.Scientific reports · 2015Article
- Regulatory networks in retinal ischemia-reperfusion injury.BMC genetics · 2015Article
- Quantitative assessment and validation of network inference methods in bioinformatics.Frontiers in genetics · 2014Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Analysis of the biological gene networks involved in a disease may lead to the identification of therapeutic targets. Such analysis requires exploring network properties, in particular the importance of individual network nodes (i.e., genes). There are many measures that consider the importance of nodes in a network and some may shed light on the biological significance and potential optimality of a gene or set of genes as therapeutic targets. This has been shown to be the case in cancer therapy. A dilemma exists, however, in finding the best therapeutic targets based on network analysis since the optimal targets should be nodes that are highly influential in, but not toxic to, the functioning of the entire network. In addition, cancer therapeutics targeting a single gene often result in relapse since compensatory, feedback and redundancy loops in the network may offset the activity associated with the targeted gene. Thus, multiple genes reflecting parallel functional cascades in a network should be targeted simultaneously, but require the identification of such targets. We propose a methodology that exploits centrality statistics characterizing the importance of nodes within a gene network that is constructed from the gene expression patterns in that network. We consider centrality measures based on both graph theory and spectral graph theory. We also consider the origins of a network topology, and show how different available representations yield different node importance results. We apply our techniques to tumor gene expression data and suggest that the identification of optimal therapeutic targets involving particular genes, pathways and sub-networks based on an analysis of the nodes in that network is possible and can facilitate individualized cancer treatments. The proposed methods also have the potential to identify candidate cancer therapeutic targets that are not thought to be oncogenes but nonetheless play important roles in the functioning of a cancer-related network or pathway.
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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.