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Supervised Dimension Reduction for Large-Scale “Omics” Data With Censored Survival Outcomes Under Possible Non-Proportional Hazards

IEEE/ACM Transactions on Computational Biology and Bioinformatics(2020)10.1109/tcbb.2020.2965934Source: DataRank Database

Supervised Dimension Reduction for Large-Scale “Omics” Data With Censored Survival Outcomes Under Possible Non-Proportional Hazards is a research paper published in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020). On theSindex it has a DataRank of 0. It has been cited 4 times.

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4 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (2 grants)Impact (FWCI 0.15)OA: closedTopics (39)IDs (PubMed, OpenAlex)SDGs (2)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

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Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

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Authors (2)

Karthik Devarajan ORCID,Lauren Spirko‐Burns ORCID

Live enrichment

PubMed:31940547 OpenAlex:https://openalex.org/W2999342380

National Institutes of Health

Grant: P30 CA006927

National Institutes of Health

Grant: 5P30CA006927-49

Transgenic Mouse

FWCI

0.15

Citation Percentile

0.4%

Citation Trend

2021

2025

closedLicense: IEEE Copyright

CLOSEDhttps://www.biorxiv.org/content/biorxiv/early/2019/06/13/586529.full.pdf

publisherhttp://xplorestaging.ieee.org/ielx7/8857/9563006/08955866.pdf?arnumber=8955866

journalhttps://doi.org/10.1109/tcbb.2020.2965934

repositoryhttps://pubmed.ncbi.nlm.nih.gov/31940547

https://biostats.bepress.com/cgi/viewcontent.cgi?article=1175&context=cobra

Fields of Study

Gene expression and cancer classificationMolecular Biology Techniques and ApplicationsBioinformatics and Genomic NetworksComputer ScienceMedicineBiology0206 medical engineering02 engineering and technology0101 mathematics01 natural sciences

MeSH Terms

Supervised Machine LearningAlgorithmsHumansNeoplasmsPrognosisComputational Biology

Keywords

Accelerated failure time modelComputer scienceFeature selectionParametric statisticsRegressionData miningProportional hazards modelOmicsScale (ratio)Dimensionality reductionBig dataMachine learningBioinformaticsStatisticsBiologyMathematicsCovariateNeoplasmsComputational BiologyHumansSupervised Machine LearningPrognosisAlgorithms

Sustainable Development Goals

SDG 3: 3. Good healthSDG 0: Peace, Justice and strong institutions

Supervised Dimension Reduction for Large-Scale “Omics” Data With Censored Survival Outcomes Under Possible Non-Proportional Hazards

FAIR Checklist

Authors (2)

Funding (2 grants)

Impact

Open Access

Topics