ArticleBMC systems biology2010
Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networks.
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 42 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
42 citing papers in PubMed, 92 citations in OpenAlex.
- A spatially informed matrix normal model for gene co-expression analysis in spatial transcriptomics studies.Nucleic acids research · 2025Article
- A powerful framework for differential co-expression analysis of general risk factors.Bioinformatics (Oxford, England) · 2025Article
- WNKs regulate mouse behavior and alter central nervous system glucose uptake and insulin signaling.bioRxiv : the preprint server for biology · 2025Article
- Alzheimer's disease-specific transcriptomic and epigenomic changes in the tryptophan catabolic pathway.Alzheimer's research & therapy · 2024Article
- The influences of ApoE isoforms on endothelial adherens junctions and actin cytoskeleton responding to mCRP.Angiogenesis · 2024Article
- Identification of mitochondrial-related genes as potential biomarkers for the subtyping and prediction of Alzheimer's disease.Frontiers in molecular neuroscience · 2023Article
- Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin.BMC genomics · 2022Article
- Identification of Differential Genes of DNA Methylation Associated With Alzheimer's Disease Based on Integrated Bioinformatics and Its Diagnostic Significance.Frontiers in aging neuroscience · 2022Article
- Integrative enrichment analysis of gene expression based on an artificial neuron.BMC medical genomics · 2021Article
- MicroRNAs as Candidate Biomarkers for Alzheimer's Disease.Non-coding RNA · 2021Review
- Inclusion of African American/Black adults in a pilot brain proteomics study of Alzheimer's disease.Neurobiology of disease · 2020Article
- AmpliSeq Transcriptome of Laser Captured Neurons from Alzheimer Brain: Comparison of Single Cell Versus Neuron Pools.Aging and disease · 2019Article
- Identifying genuine protein-protein interactions within communities of gene co-expression networks using a deconvolution method.IET systems biology · 2019Article
- Alzheimer's disease-associated (hydroxy)methylomic changes in the brain and blood.Clinical epigenetics · 2019Article
- The basis of cellular and regional vulnerability in Alzheimer's disease.Acta neuropathologica · 2019Review
- Regional protein expression in human Alzheimer's brain correlates with disease severity.Communications biology · 2019Article
- Contourlet-based hippocampal magnetic resonance imaging texture features for multivariant classification and prediction of Alzheimer's disease.Metabolic brain disease · 2018Article
- MultiDCoX: Multi-factor analysis of differential co-expression.BMC bioinformatics · 2017Article
- A comprehensive analysis on preservation patterns of gene co-expression networks during Alzheimer's disease progression.BMC bioinformatics · 2017Article
- Three-way interaction model to trace the mechanisms involved in Alzheimer's disease transgenic mice.PloS one · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder involving variations in the transcriptome of many genes. AD does not affect all brain regions simultaneously. Identifying the differences among the affected regions may shed more light onto the disease progression. We developed a novel method involving the differential topology of gene coexpression networks to understand the association among affected regions and disease severity.
methodsWe analysed microarray data of four regions--entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC) and middle temporal gyrus (MTG) from AD affected and normal subjects. A coexpression network was built for each region and the topological overlap between them was examined. Genes with zero topological overlap between two region-specific networks were used to characterise the differences between the two regions. RESULTS AND
conclusionResults indicate that MTG shows early AD pathology compared to the other regions. We postulate that if the MTG gets affected later in the disease, post-mortem analyses of individuals with end-stage AD will show signs of early AD in the MTG, while the EC, HIP and PCC will have severe pathology. Such knowledge is useful for data collection in clinical studies where sample selection is a limiting factor as well as highlighting the underlying biology of disease progression.
Indexed as
Identifiers
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.