Talks & Posters

Exploring L1 and L2 Regularization Techniques For Combining Learning and Feature Selection on Cancer Imaging Data, at Massachusetts Institute of Technology , Monday, December 11, 2017

 We developed a statistical learning approach to predict head and neck cancer response to radiation therapy using PET medical imaging data. PET imaging looks at glucose metabolism, which allows cancer cells to proliferate, with the help of a tracer. Glucose metabolism is known to be highly indicative of treatment response and patient survival. We have utilized publically available clinical trial data from The Cancer Imaging Archive (http://doi.org/10.7937/K9/TCIA.2017.umz8dv6s) as our training data. Inherently, imaging data of this type has hundreds of potential features; however,...

Read more about Exploring L1 and L2 Regularization Techniques For Combining Learning and Feature Selection on Cancer Imaging Data
Data Mining Diverse Compendia of Triple Negative Breast Cancer Samples for Improved Tumor Subtyping, at Bioinformatics of Disease and Treatment Session @ The 24th Annual International Conference on Intelligent Systems for Molecular Biology (2016), Monday, July 11, 2016:

Program Website

This work was a collaboration between the Bult Group at The Jackson Laboratory and the Hibbs Group at Trinity University. 

Read more about Data Mining Diverse Compendia of Triple Negative Breast Cancer Samples for Improved Tumor Subtyping

Pages