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ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.06.20.545686Source: DataRank Database

ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0.

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Enrichment:Funding (6 grants)OA: greenTopics (15)IDs (PubMed)SDGs (5)

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Authors (6)

Claudia Schwarz ORCID,Catharina Lange ORCID,Esa Pitkänen ORCID,Eero Vuoksimaa ORCID,the Alzheimer’s Disease Neuroimaging Initiative

Live enrichment

PubMed:38576196

Canadian Institutes of Health Research

Grant: unidentified

unidentified

Research Council of Finland

Grant: 328890

Deep probabilistic modeling of somatic mutagenesis

Research Council of Finland

Grant: 320109

Cognitive aging from middle age to the oldest-old

Research Council of Finland

Grant: 314639

Cognitive aging from middle age to the oldest-old

Research Council of Finland

Grant: 345988

Cognitive aging from middle age to the oldest-old

Research Council of Finland

Grant: 322675

Deep probabilistic modeling of somatic mutagenesis

greenLicense: CC BY

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/06/26/2023.06.20.545686.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.06.20.545686

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/10327176

https://doi.org/10.1101/2023.06.20.545686

https://doi.org/10.1111/ejn.16332

Fields of Study

02 engineering and technology0202 electrical engineering, electronic engineering, information engineering

Keywords

MaleAged, 80 and overAmyloid beta-PeptidesMemory, EpisodicBrainMiddle AgedDeep LearningAlzheimer DiseasePositron-Emission TomographyHumansFemaleNeural Networks, ComputerAged

Sustainable Development Goals

SDG 3: 3. Good healthSDG 4: 4. EducationSDG 13: 13. Climate actionSDG 15: 15. Life on landSDG 16: 16. Peace & justice