Affiliation:
1. Department of Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, Newark, NJ 07107, United States
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 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.
Publisher
Bentham Science Publishers Ltd.
Subject
Genetics (clinical),Genetics
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献