Machine learning approaches to drug response prediction: challenges and recent progress
By George Adam, Ladislav Rampášek, Z. Safikhani, P. Smirnov, Benjamin Haibe-Kains, Anna Goldenberg. npj Precision Oncology, 2020
326 citations recorded by the source corpus.
Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great promise to improve patient’s chances of successful recovery. Unfortunately, the computational task of predicting drug response is very challenging, partially due to the limitations of the available data and partially due to algorithmic shortcomings. The recent advances in deep learning may open a new chapter in the search for computational drug response prediction models and ultimately result in more accurate tools for therapy response. This review provides an overview of the computational challenges and advances in drug response prediction, and focuses on comparing the machine learning techniques to be of utmost practical use for clinicians and machine learning non-experts. The incorporation of new data modalities such as single-cell profiling, along with techniques that rapidly find effective drug combinations will likely be instrumental in improving cancer care.
Subfields: Biophysics, Cancer Research, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/84fb6e442e1e7e7b60df27214ff4c1d2ee2a53b1
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Citations in the corpus
- Predicting cellular responses to complex perturbations in high‐throughput screens (2023, background): “Combinatorial drug therapies are hypothesized to address the limited effectiveness of mono-therapies (Menden et al , 2019) and prevent drug resistance in cancer therapies (Jia et al , 2009; Menden et al , 2019; Adam et al , 2020).”
- Deep learning in cancer diagnosis, prognosis and treatment selection (2021, background): “The increasing availability of public omics data has facilitated recent growth of DL applications in cancer pharmacogenomics [155 – 157].”