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Träfflista för sökning "WFRF:(Spalletta Gianfranco) srt2:(2015-2019)"

Search: WFRF:(Spalletta Gianfranco) > (2015-2019)

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1.
  • Jones, Lesley, et al. (author)
  • Convergent genetic and expression data implicate immunity in Alzheimer's disease
  • 2015
  • In: Alzheimer's & Dementia. - : Wiley. - 1552-5260 .- 1552-5279. ; 11:6, s. 658-671
  • Journal article (peer-reviewed)abstract
    • Background: Late-onset Alzheimer's disease (AD) is heritable with 20 genes showing genome-wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease, we extended these genetic data in a pathway analysis. Methods: The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain. Results: ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (P = 3.27 X 10(-12) after multiple testing correction for pathways), regulation of endocytosis (P = 1.31 X 10(-11)), cholesterol transport (P = 2.96 X 10(-9)), and proteasome-ubiquitin activity (P = 1.34 X 10(-6)). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected P = .002-.05). Conclusions: The immime response, regulation of endocytosis, cholesterol transport, and protein ubiquitination represent prime targets for AD therapeutics.
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2.
  • Petrov, Dmitry, et al. (author)
  • Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
  • 2017
  • In: Machine learning in medical imaging. MLMI (Workshop). - Cham : Springer International Publishing. ; 10541, s. 371-378
  • Journal article (peer-reviewed)abstract
    • As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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