ArticleFrontiers in genetics2020
Module Analysis Using Single-Patient Differential Expression Signatures Improves the Power of Association Studies for Alzheimer's Disease.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deep joint learning diagnosis of Alzheimer's disease based on multimodal feature fusion.BioData mining · 2024Article
- Module Analysis Using Single-Patient Differential Expression Signatures Improves the Power of Association Studies for Alzheimer's Disease.Frontiers in genetics · 2020Article
- Single-subject proteomic signatures in Alzheimer's disease reflect clinical phenotypes and distinguish asymptomatic from symptomatic cases.Alzheimer's & dementia (New York, N. Y.)Article
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Authors and funding
3 authors.
Funding
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Abstract
The causal mechanism of Alzheimer's disease is extremely complex. Achieving great statistical power in association studies usually requires a large number of samples. In this work, we illustrated a different strategy to identify AD risk genes by clustering AD patients into modules based on their single-patient differential expression signatures. The evaluation suggested that our method could enrich AD patients with similar clinical manifestations. Applying this to a cohort of only 310 AD patients, we identified 174 AD risk loci at a strict threshold of empirical
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