The prediction of the cancer cell lines sensitivity to a specific treatment is one of the current challenges in precision medicine. With omics and pharmacogenomics data being available for over 1000 cancer cell lines, several machine learning and deep learning algorithms have been proposed for drug sensitivity prediction. However, deciding which omics data to use and which computational methods can efficiently incorporate data from different sources is the challenge which several research groups are working on. In this review, we summarize recent advances in the representative computational methods that have been developed in the last 2 years on three public datasets: COSMIC, CCLE, NCI-60. These methods aim to improve the prediction of the cancer cell lines sensitivity to a given treatment by incorporating drug's chemical information in the input or using a priori feature selection. Finally, we discuss the latest published method which aims to improve the prediction of clinical drug response of real patients starting from cancer cell line molecular profiles.

Pepe, G., Carrino, C., Parca, L., Helmer-Citterich, M. (2022). Dissecting the Genome for Drug Response Prediction. In Data mining techniques for the life technologies (pp. 187-196). springer [10.1007/978-1-0716-2095-3_7].

Dissecting the Genome for Drug Response Prediction

Helmer-Citterich, Manuela
2022

Abstract

The prediction of the cancer cell lines sensitivity to a specific treatment is one of the current challenges in precision medicine. With omics and pharmacogenomics data being available for over 1000 cancer cell lines, several machine learning and deep learning algorithms have been proposed for drug sensitivity prediction. However, deciding which omics data to use and which computational methods can efficiently incorporate data from different sources is the challenge which several research groups are working on. In this review, we summarize recent advances in the representative computational methods that have been developed in the last 2 years on three public datasets: COSMIC, CCLE, NCI-60. These methods aim to improve the prediction of the cancer cell lines sensitivity to a given treatment by incorporating drug's chemical information in the input or using a priori feature selection. Finally, we discuss the latest published method which aims to improve the prediction of clinical drug response of real patients starting from cancer cell line molecular profiles.
Settore BIO/11
English
Rilevanza internazionale
Capitolo o saggio
Cancer cell lines
Deep learning
Drug screening
Drug sensitivity prediction
Feature selection
Machine learning
Precision medicine
Algorithms
Cell Line, Tumor
Humans
Machine Learning
Pharmacogenetics
Biological Phenomena
Precision Medicine
Pepe, G., Carrino, C., Parca, L., Helmer-Citterich, M. (2022). Dissecting the Genome for Drug Response Prediction. In Data mining techniques for the life technologies (pp. 187-196). springer [10.1007/978-1-0716-2095-3_7].
Pepe, G; Carrino, C; Parca, L; Helmer-Citterich, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/307757
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