Machine learning in the prediction of cancer therapy
Rafique, Raihan ; Islam, S. M.Riazul and Kazi, Julhash U. LU
(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.
(Less)
- author
- Rafique, Raihan
; Islam, S. M.Riazul
and Kazi, Julhash U.
LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- 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
- 2024-04-20 10:08:23
@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}},
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}},
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}},
}