ArticleBMC bioinformatics2009
Analysis of AML genes in dysregulated molecular networks.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
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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.
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Who cites it
12 citing papers in PubMed, 23 citations in OpenAlex.
- Exploring pathway interactions to detect molecular mechanisms of disease: 22q11.2 deletion syndrome.Orphanet journal of rare diseases · 2023Article
- Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches.Briefings in bioinformatics · 2019Review
- A scalable method for molecular network reconstruction identifies properties of targets and mutations in acute myeloid leukemia.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers.Journal of cellular and molecular medicine · 2015Review
- An integrated approach of gene expression and DNA-methylation profiles of WNT signaling genes uncovers novel prognostic markers in acute myeloid leukemia.BMC bioinformatics · 2015Article
- Hope for GWAS: relevant risk genes uncovered from GWAS statistical noise.International journal of molecular sciences · 2014Article
- Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.Pharmacology & therapeutics · 2013Review
- Bridging the Gap between Genotype and Phenotype via Network Approaches.Frontiers in genetics · 2012Article
- Chapter 5: Network biology approach to complex diseases.PLoS computational biology · 2012Article
- Modeling information flow in biological networks.Physical biology · 2011Review
- Selected proceedings of the 2009 Summit on Translational Bioinformatics.BMC bioinformatics · 2009Article
- Systems biology and the future of medicine.Wiley interdisciplinary reviews. Systems biology and medicineArticle
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 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying disease causing genes and understanding their molecular mechanisms are essential to developing effective therapeutics. Thus, several computational methods have been proposed to prioritize candidate disease genes by integrating different data types, including sequence information, biomedical literature, and pathway information. Recently, molecular interaction networks have been incorporated to predict disease genes, but most of those methods do not utilize invaluable disease-specific information available in mRNA expression profiles of patient samples.
resultsThrough the integration of protein-protein interaction networks and gene expression profiles of acute myeloid leukemia (AML) patients, we identified subnetworks of interacting proteins dysregulated in AML and characterized known mutation genes causally implicated to AML embedded in the subnetworks. The analysis shows that the set of extracted subnetworks is a reservoir rich in AML genes reflecting key leukemogenic processes such as myeloid differentiation.
conclusionWe showed that the integrative approach both utilizing gene expression profiles and molecular networks could identify AML causing genes most of which were not detectable with gene expression analysis alone due to the minor changes in mRNA level.
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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.