Machine learning in the prediction of cancer therapy

• 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Andra medicinska och farmaceutiska grundvetenskaper (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Other Basic Medicine (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

Nyckelord

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

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