A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease | Zendy

Hongjiu Zhang | Zendy; Fan Zhu | Zendy; Hiroko H. Dodge | Zendy; Gerald A. Higgins | Zendy; Gilbert S. Omenn | Zendy; Yuanfang Guan | Zendy

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A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease

Author(s) -

Hongjiu Zhang,

Fan Zhu,

Hiroko H. Dodge,

Gerald A. Higgins,

Gilbert S. Omenn,

Yuanfang Guan

Publication year - 2018

Publication title -

gigascience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.947

H-Index - 54

ISSN - 2047-217X

DOI - 10.1093/gigascience/giy085

Subject(s) - disease , similarity (geometry) , cohort , medicine , leverage (statistics) , correlation , receiver operating characteristic , alzheimer's disease , semantic similarity , computer science , artificial intelligence , machine learning , pathology , mathematics , geometry , image (mathematics)

Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost benefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantitative diagnostic criteria for ancillary tests remains challenging.

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