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. 2019 Dec 14;22(1):346–359. doi: 10.1093/bib/bbz153

Table 1.

An overview and comparison of related reviews

Reviews Drug response prediction Data integration Summary of recent ML methods Experimental comparison
Computational models for predicting drug responses in cancer research [6]
Algorithms for drug sensitivity prediction [7]
A review on machine learning principles for multi-view biological data integration [8]
More is better: recent progress in multi-omics data integration methods [9]
Comparison and evaluation of integrative methods for the analysis of multilevel omics data: a study based on simulated and experimental cancer data [10]
Machine learning and feature selection for drug response prediction in precision oncology applications [11]
Improving drug response prediction by integrating multiple heterogeneous data sources from machine learning view point (this review)