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West MD, Labat I, Sternberg H, et al. Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of . Oncotarget. 2017;9(8):7796-7811doi: 10.18632/oncotarget.23748.
West, M. D., Labat, I., Sternberg, H., Larocca, D., Nasonkin, I., Chapman, K. B., Singh, R., Makarev, E., Aliper, A., Kazennov, A., Alekseenko, A., Shuvalov, N., Cheskidova, E., Alekseev, A., Artemov, A., Putin, E., Mamoshina, P., Pryanichnikov, N., Larocca, J., Copeland, K., Izumchenko, E., Korzinkin, M., & Zhavoronkov, A. (2018). Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of . Oncotarget, 9(8), 7796-7811. https://doi.org/10.18632/oncotarget.23748
West, Michael D, et al. "Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of ." Oncotarget vol. 9,8 (2018): 7796-7811. doi: https://doi.org/10.18632/oncotarget.23748
West MD, Labat I, Sternberg H, Larocca D, Nasonkin I, Chapman KB, Singh R, Makarev E, Aliper A, Kazennov A, Alekseenko A, Shuvalov N, Cheskidova E, Alekseev A, Artemov A, Putin E, Mamoshina P, Pryanichnikov N, Larocca J, Copeland K, Izumchenko E, Korzinkin M, Zhavoronkov A. Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of . Oncotarget. 2017 Dec 28;9(8):7796-7811. doi: 10.18632/oncotarget.23748. eCollection 2018 Jan 30. PMID: 29487692; PMCID: PMC5814259.
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Oncotarget. 2017 Dec 28;9(8):7796-7811. doi: 10.18632/oncotarget.23748. eCollection 2018 Jan 30.

Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of .

Oncotarget

Michael D West, Ivan Labat, Hal Sternberg, Dana Larocca, Igor Nasonkin, Karen B Chapman, Ratnesh Singh, Eugene Makarev, Alex Aliper, Andrey Kazennov, Andrey Alekseenko, Nikolai Shuvalov, Evgenia Cheskidova, Aleksandr Alekseev, Artem Artemov, Evgeny Putin, Polina Mamoshina, Nikita Pryanichnikov, Jacob Larocca, Karen Copeland, Evgeny Izumchenko, Mikhail Korzinkin, Alex Zhavoronkov

Affiliations

  1. AgeX Therapeutics, Inc., Alameda, CA, USA.
  2. BioTime, Inc., Alameda, CA, USA.
  3. Johns Hopkins University, Baltimore, MD, USA.
  4. Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, MD, USA.
  5. Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
  6. Innopolis University, Innoplis, Russia.
  7. Computer Technologies Lab, ITMO University, St. Petersburg, Russia.
  8. Boulder Statistics, Boulder, CO, USA.
  9. Johns Hopkins University, School of Medicine, Department of Otolaryngology-Head and Neck Cancer Research, Baltimore, MD, USA.
  10. The Biogerontology Research Foundation, Trevissome Park, Truro, UK.

PMID: 29487692 PMCID: PMC5814259 DOI: 10.18632/oncotarget.23748

Abstract

Here we present the application of deep neural network (DNN) ensembles trained on transcriptomic data to identify the novel markers associated with the mammalian embryonic-fetal transition (EFT). Molecular markers of this process could provide important insights into regulatory mechanisms of normal development, epimorphic tissue regeneration and cancer. Subsequent analysis of the most significant genes behind the DNNs classifier on an independent dataset of adult-derived and human embryonic stem cell (hESC)-derived progenitor cell lines led to the identification of

Keywords: Warburg effect; cancer marker; deep neural network; embryonic-fetal transition; stem cells

Conflict of interest statement

CONFLICTS OF INTEREST Michael D. West, Ivan Labat, Hal Sternberg, Dana Larocca, Igor Nasonkin, Karen B. Chapman, and Ratnesh Singh have financial interest, stock or stock options granted in AgeX Thera

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