Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells

HIGHLIGHTS

SUMMARY

For a given tumor entity, there is significant molecular variability across patients, which is referred to as inter-patient tumoral heterogeneity (Sanchez-Vega et_al, 2018). Third, under therapeutic pressure, tumors adapt through (epi-) genetic changes, which is defined as temporal heterogeneity (Venkatesan et_al, 2017; Yu et_al, 2021). Various computational strategies are used to develop drug sensitivity prediction models, mainly machine_learning based methods which include matrix factorization, support vector machines, random forests, and deep learning (Dong et_al, 2015; Kim et_al, 2019; Lind and Anderson, 2019). Deep learning (DL) is a subset of machine_learning that is . . .

 



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