Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.

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A convolutional neural network to filter artifacts in spectroscopic MRI.Predicting cancer outcomes from histology and genomics using convolutional networks.Multi-faceted computational assessment of risk and progression in oligodendroglioma implicates NOTCH and PI3K pathways

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P2860

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.

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http://www.wikidata.org/entity/Q41718329

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2017 nî lūn-bûn

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2017年の論文

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Predicting clinical outcomes f ...... les with deep survival models.

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Predicting clinical outcomes f ...... les with deep survival models.

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Predicting clinical outcomes f ...... les with deep survival models.

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Predicting clinical outcomes f ...... les with deep survival models.

@en

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Chengliang Dong

http://www.w3.org/2001/XMLSchema#string

Congzheng Song

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Daniel J Brat

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David A Gutman

http://www.w3.org/2001/XMLSchema#string

Fatemeh Amrollahi

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Jose Enrique Velazquez Vega

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Joshua E Lewis

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Safoora Yousefi

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Sameer H Halani

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P2860

Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

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Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1

Molecular mechanisms of epithelial-mesenchymal transition

Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas.

Deep learning

The transcriptional network for mesenchymal transformation of brain tumours.

Most human non-GCIMP glioblastoma subtypes evolve from a common proneural-like precursor glioma

Mammostrat as a tool to stratify breast cancer patients at risk of recurrence during endocrine therapy.

Random survival forests for competing risks.

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s41598-017-11817-6

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scholarly article

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10.1038/S41598-017-11817-6

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English

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http://www.w3.org/2001/XMLSchema#string

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Mohamed Amgad

Lee A D Cooper

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2017-09-15T00:00:00Z

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1091665734

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Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.

about

P2860

P2860

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.

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