Current Advances and Limitations of Deep Learning in Anticancer Drug Sensitivity Prediction.

Abstract:

:Anticancer drug screening can accelerate drug discovery to save the lives of cancer patients, but cancer heterogeneity makes this screening challenging. The prediction of anticancer drug sensitivity is useful for anticancer drug development and the identification of biomarkers of drug sensitivity. Deep learning, as a branch of machine learning, is an important aspect of in silico research. Its outstanding computational performance means that it has been used for many biomedical purposes, such as medical image interpretation, biological sequence analysis, and drug discovery. Several studies have predicted anticancer drug sensitivity based on deep learning algorithms. The field of deep learning has made progress regarding model performance and multi-omics data integration. However, deep learning is limited by the number of studies performed and data sources available, so it is not perfect as a pre-clinical approach for use in the anticancer drug screening process. Improving the performance of deep learning models is a pressing issue for researchers. In this review, we introduce the research of anticancer drug sensitivity prediction and the use of deep learning in this research area. To provide a reference for future research, we also review some common data sources and machine learning methods. Lastly, we discuss the advantages and disadvantages of deep learning, as well as the limitations and future perspectives regarding this approach.

journal_name

Curr Top Med Chem

authors

Tan X,Yu Y,Duan K,Zhang J,Sun P,Sun H

doi

10.2174/1568026620666200710101307

subject

Has Abstract

pub_date

2020-01-01 00:00:00

pages

1858-1867

issue

21

eissn

1568-0266

issn

1873-4294

pii

CTMC-EPUB-108033

journal_volume

20

pub_type

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