Background:
In recent years, the availability of high throughput technologies, establishment of large molecular
patient data repositories, and advancement in computing power and storage have allowed elucidation of complex
mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside
experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective
learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning
design.
Objective:
In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer,
including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview
their mathematical foundations and discuss their limitations and alternative approaches all in light of their application to
therapeutic response modeling in cancer.
Conclusion:
We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient
data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic
response modeling.
Comparative investigation of RSM and ANN for multi-response modeling and optimization studies of derived chitosan from Archachatina marginata shell