Introduction to Bayesian Response Modeling

2010 ◽  
pp. 1-29
Author(s):  
Jean-Paul Fox
Keyword(s):  
Response Modeling

Item response modeling of longitudinal personality data

2004 ◽  
Author(s):  
Kate E. Walton ◽  
Brent W. Roberts ◽  
Avshalom Caspi ◽  
Terrie E. Moffitt
Keyword(s):  
Item Response ◽  
Item Response Modeling ◽  
Response Modeling

Predicting Advertising Success Beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling

2014 ◽  
Author(s):  
Vinod Venkatraman ◽  
Angelika Dimoka ◽  
Paul A. Pavlou ◽  
Khoi Vo ◽  
William Hampton ◽  
...  
Keyword(s):  
Market Response ◽  
Response Modeling

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

2020 ◽  
Vol 21 ◽  
Author(s):  
Sukanya Panja ◽  
Sarra Rahem ◽  
Cassandra J. Chu ◽  
Antonina Mitrofanova
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Therapeutic Response ◽  
Patient Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Human Machine Interaction ◽  
Data Repositories ◽  
Response Modeling

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.

Exposure-response modeling of non-cancer effects in humans exposed to Libby Amphibole Asbestos; update

2015 ◽  
Vol 73 (3) ◽  
pp. 780-789 ◽  
Author(s):  
Robert Benson ◽  
David Berry ◽  
James Lockey ◽  
William Brattin ◽  
Timothy Hilbert ◽  
...  
Keyword(s):  
Exposure Response ◽  
Amphibole Asbestos ◽  
Libby Amphibole ◽  
Response Modeling
Sign in / Sign up

Export Citation Format

Share Document