Cancer Communications >

2024 , Vol. 44 >Issue 5: 584 - 588

DOI: https://doi.org/10.1002/cac2.12530

Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable Artificial Intelligence

  • Debaditya Chakraborty , 1 ,
  • Elizabeth Gutierrez-Chakraborty 2 ,
  • Cristian Rodriguez-Aguayo 3 ,
  • Hakan Başağaoğlu 4 ,
  • Gabriel Lopez-Berestein 3 ,
  • Paola Amero , 3
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  • 1. School of Civil and Environmental Engineering and Construction Management, The University of Texas at San Antonio, San Antonio, Texas, USA
  • 2. Department of Cell Systems and Anatomy, University of Texas Health Science Center, San Antonio, Texas, USA
  • 3. Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
  • 4. Edwards Aquifer Authority, San Antonio, Texas, USA
debaditya.chakraborty@utsa.edu
PAmero@mdanderson.org

Received date: 24 Jul 2023

Revised date: 12 Jan 2024

Accepted date: 29 Feb 2024

Published date: 20 May 2024

Copyright

2024 2024 The Authors. Cancer Communications published by John Wiley & Sons Australia, Ltd on behalf of Sun Yat-sen University Cancer Center.
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Cite this article

Debaditya Chakraborty , Elizabeth Gutierrez-Chakraborty , Cristian Rodriguez-Aguayo , Hakan Başağaoğlu , Gabriel Lopez-Berestein , Paola Amero . Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable Artificial Intelligence[J]. Cancer Communications, 2024 , 44(5) : 584 -588 . DOI: 10.1002/cac2.12530

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