TY - JOUR
T1 - Machine learning in the prediction of cancer therapy
AU - Rafique, Raihan
AU - Islam, S. M.Riazul
AU - Kazi, Julhash U.
N1 - 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)
PY - 2021/7/20
Y1 - 2021/7/20
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Convolutional neural network
KW - Deep learning
KW - Deep neural network
KW - Drug combinations
KW - Drug synergy
KW - Elastic net
KW - Factorization machine
KW - Graph convolutional network
KW - Higher-order factorization machines
KW - Lasso
KW - Matrix factorization
KW - Monotherapy prediction
KW - Ordinary differential equation
KW - Random forests
KW - Restricted Boltzmann machine
KW - Ridge regression
KW - Support vector machines
KW - Variational autoencoder
KW - Visible neural network
UR - http://www.scopus.com/inward/record.url?scp=85110763754&partnerID=8YFLogxK
U2 - 10.1016/j.csbj.2021.07.003
DO - 10.1016/j.csbj.2021.07.003
M3 - Review article
AN - SCOPUS:85110763754
VL - 19
SP - 4003
EP - 4017
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
SN - 2001-0370
ER -