Computational genetics analysis of grey matter density in Alzheimer’s disease
Amanda Zieselman; Jonathan Fisher; Ting Hu; Peter C. Andrews; Casey S. Greene; Li Shen; Andrew J. Saykin; Jason H. Moore · 2014 · BioData Mining
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Alzheimer's disease is the most common form of progressive dementia and there is currently no known cure. The cause of onset is not fully understood but genetic factors are expected to play a significant role. We present here a bioinformatics approach to the genetic analysis of grey matter density as an endophenotype for late onset Alzheimer's disease. Our approach combines machine learning analysis of gene-gene interactions with large-scale functional genomics data for assessing biological relationships. RESULTS: We found a statistically significant synergistic interaction among t
Abstract by Amanda Zieselman; Jonathan Fisher; Ting Hu; Peter C. Andrews; Casey S. Greene; Li Shen; Andrew J. Saykin; Jason H. Moore, BioData Mining (2014) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/1756-0381-7-17