Evidence map›Paper›PMID 29596980›Full record

ArticleNeuroImage2018

SMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data.

Leo Yu-Feng Liu, Yufeng Liu, Hongtu Zhu, Alzheimer's Disease Neuroimaging Initiative

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Sparse Principal Component based High-Dimensional Mediation Analysis.Computational statistics & data analysis · 2020
    Article
  2. 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

4 authors.

Leo Yu-Feng LiuDepartment of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Yufeng LiuDepartment of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center (LCCC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Hongtu ZhuDepartment of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: hzhu5@mdanderson.org.
Alzheimer's Disease Neuroimaging Initiative

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI Duygu Tosun-Turgut · 2016 to 2026
$226.7M
Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
Statistical Analysis of Biomedical Imaging Data in Curved SpaceR01MH086633 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI IBRAHIM, JOSEPH G · 2010 to 2018
$2.9M
Genome-wide Identification of Variants Affecting Early Human Brain DevelopmentR01MH092335 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KNICKMEYER, REBECCA · 2011 to 2015
$2.5M
Unlocking complex co-expression network using graphical modelsR01GM126550 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIU, YUFENG · 2017 to 2020
$1.6M
NCI NIH HHS P01 CA142538NIA NIH HHS U01 AG024904NIA NIH HHS U19 AG024904NIGMS NIH HHS R01 GM126550NIMH NIH HHS R01 MH086633NIMH NIH HHS R01 MH092335
6 · The paper itself

Abstract

With the development of advanced imaging techniques, scientists are interested in identifying imaging biomarkers that are related to different subtypes or transitional stages of various cancers, neuropsychiatric diseases, and neurodegenerative diseases, among many others. In this paper, we propose a novel spatial multi-category angle-based classifier (SMAC) for the efficient identification of such imaging biomarkers. The proposed SMAC not only utilizes the spatial structure of high-dimensional imaging data but also handles both binary and multi-category classification problems. We introduce an efficient algorithm based on an alternative direction method of multipliers to solve the large-scale optimization problem for SMAC. Both our simulation and real data experiments demonstrate the usefulness of SMAC.

Indexed as

AlgorithmsAgedAged, 80 and overAlzheimer DiseaseBiomarkersBrainClassificationCognitive DysfunctionFemaleHumansImage Processing, Computer-AssistedMaleNeuroimagingBiomarkersADMMAlzheimer's diseaseAngle-based classifierFused lassoLarge margin classifierNeuroimaging classification

Identifiers

PMID29596980
PMCPMC6317520

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.