Evidence map›Paper›PMID 30010762›Full record

ArticleGigaScience2018

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

Hongjiu Zhang, Fan Zhu, Hiroko H Dodge, Gerald A Higgins, Gilbert S Omenn, Yuanfang Guan, Alzheimer's Disease Neuroimaging Initiative

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in GigaScience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hongjiu ZhangDepartment of Computational Medicine and Bioinformatics, University of Michigan, 2017G Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI, USA 48109.
Fan ZhuDepartment of Computational Medicine and Bioinformatics, University of Michigan, 2017G Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI, USA 48109.
Hiroko H DodgeMichigan Alzheimer's Disease Center, University of Michigan, 2101 Commonwealth Blvd, Ann Arbor, MI, USA 48105.
Gerald A HigginsDepartment of Computational Medicine and Bioinformatics, University of Michigan, 2017G Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI, USA 48109.
Gilbert S OmennDepartment of Computational Medicine and Bioinformatics, University of Michigan, 2017G Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI, USA 48109.
Yuanfang GuanDepartment of Computational Medicine and Bioinformatics, University of Michigan, 2017G Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI, USA 48109.
Alzheimer's Disease Neuroimaging Initiative

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Outreach, Recruitment and Education CoreP30AG008017 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI KAYE, JEFFREY A · 1990 to 2019
$27.7M
Strategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Dana Dolinoy · 2011 to 2026
$21.3M
Michigan Alzheimer's Disease Core CenterP30AG053760 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALBIN, ROGER L · 2016 to 2020
$10.1M
NIA NIH HHS P30 AG008017NIA NIH HHS P30 AG053760NIA NIH HHS U01 AG024904NIEHS NIH HHS P30 ES017885
6 · The paper itself

Abstract

Motivation: 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. Results: We have developed a similarity-based approach that matches individuals to subjects with similar conditions. We modeled the disease with a Gaussian process, and tested the method in the Alzheimer's Disease Big Data DREAM Challenge. Ranked the highest among submitted methods, our diagnostic model predicted cognitive impairment scores in an independent dataset test with a correlation score of 0.573. It differentiated AD patients from control subjects with an area under the receiver operating curve of 0.920. Without knowing longitudinal information about subjects, the model predicted patients who are vulnerable to conversion from mild-cognitive impairment to AD through the similarity network. This diagnostic framework can be applied to other diseases with clinical heterogeneity, such as Parkinson's disease.

Indexed as

AlgorithmsAlzheimer DiseaseBiomarkersCognition DisordersCognitive DysfunctionCohort StudiesDiagnosis, Computer-AssistedDisease ProgressionHumansMachine LearningMagnetic Resonance ImagingMedical InformaticsNormal DistributionParkinson DiseasePhenotypePrincipal Component AnalysisBiomarkers

Identifiers

PMID30010762
PMCPMC6054197

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.