Title:Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer
Volume: 22
Issue: 4
Author(s): Sukanya Panja, Sarra Rahem, Cassandra J. Chu and Antonina Mitrofanova*
Affiliation:
- Department of Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, Newark, NJ 07107,United States
Keywords:
Therapeutic response, therapeutic resistance, machine learning, cancer, prediction, data repositories.
Abstract:
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 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.
