Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics (Q113124839)

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Language	Label	Description	Also known as
English	Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics	research project

Statements

instance of

research project

2 references

reference URL

https://physics.cancer.gov/research-funding/utah

publisher

National Cancer Institute

Wikimedia import URL

https://en.wikipedia.org/wiki/Physical_Sciences-Oncology_Centers

image

Tensor Generalized Singular Value Decomposition following et int. Alter PLoS One 2015 and Alter NCI Physical Sciences in Oncology 2015.jpg
5,000 × 6,495; 3.05 MB

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The tensor generalized singular value decomposition is one of the multi-tensor decompositions, invented to simultaneously identify the similar and dissimilar among, and create a single coherent model from any data types, of any number and dimensions. (English)

0 references

location

University of Utah

2 references

reference URL

https://physics.cancer.gov/research-funding/utah

publisher

National Cancer Institute

reference URL

https://alterlab.org/physics_of_cancer/

main subject

multi-tensor decomposition

0 references

start time

2015

1 reference

reference URL

https://app.dimensions.ai/details/grant/grant.4455012

end time

2023

0 references

participant

Orly Alter

subject has role

principal investigator

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Carl T. Wittwer

subject has role

co-investigator

0 references

described at URL

https://alterlab.org/physics_of_cancer/

language of work or name

English

1 reference

reference URL

https://physics.cancer.gov/research-funding/utah

publisher

National Cancer Institute

described by source

Tensor GSVD of patient- and platform-matched tumor and normal DNA copy-number profiles uncovers chromosome arm-wide patterns of tumor-exclusive platform-consistent alterations encoding for cell transformation and predicting ovarian cancer survival

publication date

April 2015

0 references

DNA Copy-Number Alterations in Primary Ovarian Serous Cystadenocarcinoma Encoding for Cell Transformation and Predicting Survival and Response to Platinum Therapy Throughout the Course of the Disease

publication date

January 2016

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Platform-Independent Genome-Wide Pattern of DNA Copy-Number Alterations Predicting Astrocytoma Survival and Response to Treatment Revealed by the GSVD Formulated as a Comparative Spectral Decomposition

publication date

October 2016

0 references

Patterns of DNA Copy-Number Alterations Revealed by the GSVD and Tensor GSVD Encode for Cell Transformation and Predict Survival and Response to Platinum in Adenocarcinomas

publication date

July 2018

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Mathematically Universal and Biologically Consistent Astrocytoma Genotype Encodes for Transformation and Predicts Survival Phenotype

publication date

July 2018

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Mathematically universal and biologically consistent astrocytoma genotype encodes for transformation and predicts survival phenotype

publication date

September 2018

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GSVD- and tensor GSVD-uncovered patterns of DNA copy-number alterations predict adenocarcinomas survival in general and in response to platinum

publication date

August 2019

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Retrospective clinical trial experimentally validates glioblastoma genome-wide pattern of DNA copy-number alterations predictor of survival

publication date

May 2020

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funder

Physical Sciences in Oncology Network

part of

National Cancer Institute

1 reference

reference URL

https://physics.cancer.gov/research-funding/utah

publisher

National Cancer Institute

principal investigator

Orly Alter

2 references

reference URL

https://physics.cancer.gov/research-funding/utah

publisher

National Cancer Institute

stated in

ORCID iD

ORCID iD

0000-0002-0418-1078

retrieved

13 October 2020

 

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