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.

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
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

Islam, S. M.RiazulSejong University (author)
Kazi, Julhash U.Lund University,Lunds universitet,Avdelningen för translationell cancerforskning,Institutionen för laboratoriemedicin,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Division of Translational Cancer Research,Department of Laboratory Medicine,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments(Swepub:lu)med-kui (author)
Ideflod ABSejong University (creator_code:org_t)

In:Computational and Structural Biotechnology Journal: Elsevier BV19, s. 4003-40172001-0370

By the author/editor: Rafique, Raihan; Islam, S. M.Riaz ...; Kazi, Julhash U.

About the subject

MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Basic Medicine; and Other Basic Medi ...

MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Clinical Medicin ...; and Cancer and Oncol ...

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