ArticleScientific reports2015
Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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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
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models.Cell reports · 2020Pooled it
- The YTHDF proteins modulate Alzheimer's disease-associated brain gene signatures.Molecular neurodegeneration · 2026Article
- Integration of aged brain multi-omics reveals cross-system mechanisms underlying Alzheimer's disease heterogeneity.Cell reports · 2026Article
- Gene module-trait network analysis uncovers cell type specific systems and genes relevant to Alzheimer's Disease.Acta neuropathologica communications · 2025Article
- Integration of aged brain multi-omics reveals cross-system mechanisms underlying Alzheimer's disease heterogeneity.bioRxiv : the preprint server for biology · 2025Article
- Uncovering plaque-glia niches in human Alzheimer's disease brains using spatial transcriptomics.Molecular neurodegeneration advances · 2025Article
- Integration across biophysical scales identifies molecular and cellular correlates of person-to-person variability in human brain connectivity.Nature neuroscience · 2024Article
- The molecular genetic landscape of human brain size variation.Cell reports · 2023Article
- Dissecting the human leptomeninges at single-cell resolution.Nature communications · 2023Article
- Robust, scalable, and informative clustering for diverse biological networks.Genome biology · 2023Article
- Influence of Alzheimer's disease related neuropathology on local microenvironment gene expression in the human inferior temporal cortex.GEN biotechnology · 2023Article
- Inferring protein expression changes from mRNA in Alzheimer's dementia using deep neural networks.Nature communications · 2022Article
- A network generator for covert network structures.Information sciences · 2022Article
- Systems biology approaches to unravel the molecular and genetic architecture of Alzheimer's disease and related tauopathies.Neurobiology of disease · 2021Review
- Neuroticism alters the transcriptome of the frontal cortex to contribute to the cognitive decline and onset of Alzheimer's disease.Translational psychiatry · 2021Article
- A cortical immune network map identifies distinct microglial transcriptional programs associated with β-amyloid and Tau pathologies.Translational psychiatry · 2021Article
- Community Detection in Large-Scale Bipartite Biological Networks.Frontiers in genetics · 2021Review
- Overlapping Community Detection Based on Membership Degree Propagation.Entropy (Basel, Switzerland) · 2020Article
- A multiresolution framework to characterize single-cell state landscapes.Nature communications · 2020Article
- Identifying the molecular systems that influence cognitive resilience to Alzheimer's disease in genetically diverse mice.Learning & memory (Cold Spring Harbor, N.Y.) · 2020Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Biological functions are carried out by groups of interacting molecules, cells or tissues, known as communities. Membership in these communities may overlap when biological components are involved in multiple functions. However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional methods are sensitive to noise and parameter settings. These aspects of traditional clustering methods limit our ability to detect biological communities, and therefore our ability to understand biological functions. To address these limitations and detect robust overlapping biological communities, we propose an unorthodox clustering method called SpeakEasy which identifies communities using top-down and bottom-up approaches simultaneously. Specifically, nodes join communities based on their local connections, as well as global information about the network structure. This method can quantify the stability of each community, automatically identify the number of communities, and quickly cluster networks with hundreds of thousands of nodes. SpeakEasy shows top performance on synthetic clustering benchmarks and accurately identifies meaningful biological communities in a range of datasets, including: gene microarrays, protein interactions, sorted cell populations, electrophysiology and fMRI brain imaging.
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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.