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Data Augmentation with Cross-Modal Variational Autoencoders (DACMVA) for Cancer Survival Prediction

Information(2023)10.3390/info15010007Source: DataRank Database

Data Augmentation with Cross-Modal Variational Autoencoders (DACMVA) for Cancer Survival Prediction is a research paper published in Information (2023). On theSindex it has a DataRank of 0. It has been cited 8 times.

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0DataRank · unranked

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Open Access8 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)OA: goldTopics (9)IDs (PubMed, PMC)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (2)

Cassie S. Mitchell ORCID,Sara Rajaram

Live enrichment

PubMed:38665395 PMC:PMC11044918

National Science Foundation CAREER 10.13039/100000001

Grant: 1944247

CAREER: A systems engineering approach to elucidate and treat multi-factorial pathology

National Science Foundation CAREER 10.13039/100000001

Grant: U19-AG056169

National Science Foundation CAREER 10.13039/100000001

Grant: 253558

NIA NIH HHS

Grant: U19 AG065169

NIA NIH HHS

Grant: R01 AG056169

NIA NIH HHS

Grant: R56 AG056169

goldLicense: CC BY

publisherhttps://www.mdpi.com/2078-2489/15/1/7/pdf

Unpaywallhttps://www.mdpi.com/2078-2489/15/1/7/pdf?version=1703151064

Europe_PMChttps://europepmc.org/articles/PMC11044918

Europe_PMChttps://europepmc.org/articles/PMC11044918?pdf=render

https://doi.org/10.3390/info15010007

Fields of Study

0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Data AugmentationGenerative Adversarial NetworkVariational AutoencoderCancer Survival PredictionInformation technologyT58.5-58.64Article

Sustainable Development Goals

SDG 3: 3. Good health