Literature DB >> 32391419

Deep learning: shaping the medicine of tomorrow.

Konstantinos Vougas1,2, Spyridon Almpanis1, Vassilis Gorgoulis1,2,3,4.   

Abstract

Predicting response to therapy is a major challenge in medicine. Machine learning algorithms are promising tools for assisting this aim. Amongst them, Deep Neural Networks are emerging as the most capable of interrogating across multiple data types. Their further development will lead to sophisticated knowledge extraction, shaping the medicine of tomorrow.
© 2020 Taylor & Francis Group, LLC.

Entities:  

Keywords:  Personalized medicine; cancer treatment; deep neural networks (DNN); drug response prediction; machine learning; metastasis detection

Year:  2020        PMID： 32391419      PMCID： PMC7199753          DOI： 10.1080/23723556.2020.1723462

Source DB:  PubMed          Journal:  Mol Cell Oncol        ISSN： 2372-3556


  6 in total

Review 1.  Genomic instability--an evolving hallmark of cancer.

Authors:  Simona Negrini; Vassilis G Gorgoulis; Thanos D Halazonetis
Journal:  Nat Rev Mol Cell Biol       Date:  2010-03       Impact factor: 94.444

Review 2.  Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining.

Authors:  Konstantinos Vougas; Theodore Sakellaropoulos; Athanassios Kotsinas; George-Romanos P Foukas; Andreas Ntargaras; Filippos Koinis; Alexander Polyzos; Vassilios Myrianthopoulos; Hua Zhou; Sonali Narang; Vassilis Georgoulias; Leonidas Alexopoulos; Iannis Aifantis; Paul A Townsend; Petros Sfikakis; Rebecca Fitzgerald; Dimitris Thanos; Jiri Bartek; Russell Petty; Aristotelis Tsirigos; Vassilis G Gorgoulis
Journal:  Pharmacol Ther       Date:  2019-07-30       Impact factor: 12.310

3.  A Dengue Vaccine.

Authors:  Anna P Durbin
Journal:  Cell       Date:  2016-06-30       Impact factor: 41.582

4.  A Deep Learning Framework for Predicting Response to Therapy in Cancer.

Authors:  Theodore Sakellaropoulos; Konstantinos Vougas; Sonali Narang; Filippos Koinis; Athanassios Kotsinas; Alexander Polyzos; Tyler J Moss; Sarina Piha-Paul; Hua Zhou; Eleni Kardala; Eleni Damianidou; Leonidas G Alexopoulos; Iannis Aifantis; Paul A Townsend; Mihalis I Panayiotidis; Petros Sfikakis; Jiri Bartek; Rebecca C Fitzgerald; Dimitris Thanos; Kenna R Mills Shaw; Russell Petty; Aristotelis Tsirigos; Vassilis G Gorgoulis
Journal:  Cell Rep       Date:  2019-12-10       Impact factor: 9.423

Review 5.  An oncogene-induced DNA damage model for cancer development.

Authors:  Thanos D Halazonetis; Vassilis G Gorgoulis; Jiri Bartek
Journal:  Science       Date:  2008-03-07       Impact factor: 47.728

6.  Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines.

Authors:  Paul Geeleher; Nancy J Cox; R Stephanie Huang
Journal:  Genome Biol       Date:  2014-03-03       Impact factor: 13.583
  6 in total

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