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Machine learning in the prediction of cancer therapy

Rafique, Raihan ; Islam, S. M.Riazul and Kazi, Julhash U. LU orcid (2021) In Computational and Structural Biotechnology Journal 19. p.4003-4017
Abstract

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power,... (More)

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.

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type
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publication status
published
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keywords
Artificial intelligence, Convolutional neural network, Deep learning, Deep neural network, Drug combinations, Drug synergy, Elastic net, Factorization machine, Graph convolutional network, Higher-order factorization machines, Lasso, Matrix factorization, Monotherapy prediction, Ordinary differential equation, Random forests, Restricted Boltzmann machine, Ridge regression, Support vector machines, Variational autoencoder, Visible neural network
in
Computational and Structural Biotechnology Journal
volume
19
pages
15 pages
publisher
Research Network of Computational and Structural Biotechnology
external identifiers
  • scopus:85110763754
  • pmid:34377366
ISSN
2001-0370
DOI
10.1016/j.csbj.2021.07.003
language
English
LU publication?
yes
additional info
Funding Information: This research was supported by the Crafoord Foundation (JUK), the Swedish Cancer Society (JUK), and the Swedish Childhood Cancer Foundation (JUK). Open Access funding is provided by Lund University. Publisher Copyright: © 2021 The Author(s) Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
id
bcf0ca11-bcc8-432a-874a-22d65e0ed575
date added to LUP
2021-08-12 22:01:18
date last changed
2021-10-06 03:56:32
@article{bcf0ca11-bcc8-432a-874a-22d65e0ed575,
  abstract     = {<p>Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.</p>},
  author       = {Rafique, Raihan and Islam, S. M.Riazul and Kazi, Julhash U.},
  issn         = {2001-0370},
  language     = {eng},
  pages        = {4003--4017},
  publisher    = {Research Network of Computational and Structural Biotechnology},
  series       = {Computational and Structural Biotechnology Journal},
  title        = {Machine learning in the prediction of cancer therapy},
  url          = {http://dx.doi.org/10.1016/j.csbj.2021.07.003},
  doi          = {10.1016/j.csbj.2021.07.003},
  volume       = {19},
  year         = {2021},
}