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MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI

IEEE Access(2020)10.1109/access.2020.3025828Source: DataRank Database

MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI is a research paper published in IEEE Access (2020). On theSindex it has a DataRank of 0. It has been cited 5 times.

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Open Access5 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (8 grants)OA: goldTopics (7)IDs (PubMed, PMC)

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Authors (9)

Zhaomin Yao ORCID,Meiyu Duan ORCID,Shuai Liu ORCID,Fei Li ORCID,Zhu Hai-Yang ORCID

Live enrichment

PubMed:35548102 PMC:PMC9090182

Jilin Provincial Key Laboratory of Big Data Intelligent Computing

Grant: 20180622002JC

Education Department of Jilin Province

Grant: JJKH20180145KJ

Bioknow MedAI Institute

Grant: BMCPP-2018-001

Alzheimer’s Disease Neuroimaging Initiative (ADNI), National Institutes of Health

Grant: U01 AG024904

Department of Defense

Grant: W81XWH-12-2-0012

High-Performance Computing Center, Jilin University

Startup Grant of Jilin University

Fundamental Research Funds for the Central Universities, Jilin University

goldLicense: cc-by

publisherhttps://ieeexplore.ieee.org/ielx7/6287639/8948470/09203784.pdf

publisherhttp://xplorestaging.ieee.org/ielx7/6287639/8948470/09203784.pdf?arnumber=9203784

repositoryhttps://doaj.org/article/a41b3d73663b47aebb52020de66d8183

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/9090182

Europe_PMChttps://europepmc.org/articles/PMC9090182

Keywords

Alzheimer’s diseaseMild Cognitive ImpairmentResting-state Functional MriBrain Functional Connectivity NetworkMulti-Source Connectivity NetworkMuscnetWeighted Voting Model