A Deep Learning Framework for Predicting Response to Therapy in Cancer-Reference-Cited by-同舟云学术

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A Deep Learning Framework for Predicting Response to Therapy in Cancer

Published:2019-12 Issue:11 Volume:29 Page:3367-3373.e4
ISSN:2211-1247
Container-title:Cell Reports
language:en
Short-container-title:Cell Reports

Author:

Sakellaropoulos Theodore,Vougas Konstantinos,Narang Sonali,Koinis Filippos,Kotsinas Athanassios,Polyzos Alexander,Moss Tyler J.,Piha-Paul Sarina,Zhou Hua,Kardala Eleni,Damianidou Eleni,Alexopoulos Leonidas G.,Aifantis Iannis,Townsend Paul A.,Panayiotidis Mihalis I.,Sfikakis Petros,Bartek Jiri,Fitzgerald Rebecca C.,Thanos Dimitris,Mills Shaw Kenna R.,Petty Russell,Tsirigos Aristotelis,Gorgoulis Vassilis G.

Funder

European Union’s

Welfare Foundation for Social & Cultural Sciences

Pentagon Biotechnology

DeepMed IO

NKUA-SARG

Novo Nordisk Foundation

Danish Cancer Society

Swedish Cancerfonden

Laura and Isaac Perlmutter Cancer Center

Cancer Research UK

Publisher

Elsevier BV

Subject

General Biochemistry, Genetics and Molecular Biology

Reference37 articles.

1. Signatures of mutational processes in human cancer;Alexandrov;Nature,2013

2. Paclitaxel Through the Ages of Anticancer Therapy: Exploring Its Role in Chemoresistance and Radiation Therapy;Barbuti;Cancers (Basel),2015

3. Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer;Bibault;Sci. Rep.,2018

4. Cancer Drug Response profile scan (CDRscan): a deep learning model that predicts drug effectiveness from cancer genomic signature;Chang;Sci. Rep.,2018

5. Predicting drug response of tumors from integrated genomic profiles by deep neural networks;Chiu;BMC Med. Genomics,2019

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