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Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 6515-6530. doi: 10.3934/mbe.2020340
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Can computers conceive the complexity of cancer to cure it? Using artificial intelligence technology in cancer modelling and drug discovery

  • QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia
  • Received: 13 July 2020 Accepted: 10 September 2020 Published: 25 September 2020

  • Drug discovery and the development of safe and effective therapeutics is an intricate procedure, further complicated in the context of cancer research by the inherent heterogeneity and complexity of the disease. To address the difficulties of identifying, validating, and pursuing a promising drug target, artificial intelligence (AI) technologies including machine learning (ML) have been adopted at all stages throughout the drug development pipeline. Various methods are widely employed to efficiently process and learn from experimental data sets, with agent-based models garnering thorough interest due to their ability to model individual cell populations with aberrant phenotypes. The predictive power of artificial intelligence modelling techniques founded in comprehensive datasets and automated decision-making generates an obvious avenue of interest for application in the drug discovery pipeline.

    Citation: Rachael C. Adams, Behnam Rashidieh. Can computers conceive the complexity of cancer to cure it? Using artificial intelligence technology in cancer modelling and drug discovery[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 6515-6530. doi: 10.3934/mbe.2020340

    Related Papers:

  • Drug discovery and the development of safe and effective therapeutics is an intricate procedure, further complicated in the context of cancer research by the inherent heterogeneity and complexity of the disease. To address the difficulties of identifying, validating, and pursuing a promising drug target, artificial intelligence (AI) technologies including machine learning (ML) have been adopted at all stages throughout the drug development pipeline. Various methods are widely employed to efficiently process and learn from experimental data sets, with agent-based models garnering thorough interest due to their ability to model individual cell populations with aberrant phenotypes. The predictive power of artificial intelligence modelling techniques founded in comprehensive datasets and automated decision-making generates an obvious avenue of interest for application in the drug discovery pipeline.


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