Open AccessBig Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer
Author(s) -
Sukanya Panja,
Sarra M. Rahem,
Cassandra J. Chu,
Antonina Mitrofanova
Publication year - 2021
Publication title -
current genomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 64
eISSN - 1875-5488
pISSN - 1389-2029
DOI - 10.2174/1389202921999201224110101
Subject(s) - machine learning , computer science , artificial intelligence , big data , support vector machine , artificial neural network , logistic regression , random forest , deep learning , data science , data mining
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.