John L. Hopper

According to our database, John L. Hopper authored at least 8 papers between 2012 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2023

RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement.

Hong Hui Yeoh, Andrea Liew, Raphael C.-W. Phan, Fredrik Strand, Kartini Rahmat, Tuong Linh Nguyen, John L. Hopper, Maxine Tan

CoRR, 2023

2017

The Ark: a customizable web-based data management tool for health and medical research.

Adrian Bickerstaffe, Thilina Ranaweera, Travis Endersby, Christopher Ellis, Sanjaya Maddumarachchi, George E. Gooden, Paul White, Eric K. Moses, Alex W. Hewitt, John L. Hopper

Bioinform., 2017

2016

Bayesian Grouped Horseshoe Regression with Application to Additive Models.

Zemei Xu, Daniel F. Schmidt, Enes Makalic, Guoqi Q. Qian, John L. Hopper

Proceedings of the AI 2016: Advances in Artificial Intelligence, 2016

Approximating Message Lengths of Hierarchical Bayesian Models Using Posterior Sampling.

Daniel F. Schmidt, Enes Makalic, John L. Hopper

Proceedings of the AI 2016: Advances in Artificial Intelligence, 2016

Bayesian Robust Regression with the Horseshoe+ Estimator.

Enes Makalic, Daniel F. Schmidt, John L. Hopper

Proceedings of the AI 2016: Advances in Artificial Intelligence, 2016

2015

High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies.

Benjamin Goudey, Mani Abedini, John L. Hopper, Michael Inouye, Enes Makalic, Daniel F. Schmidt, John Wagner, Zeyu Zhou, Justin Zobel, Matthias Reumann

Health Inf. Sci. Syst., 2015

2013

DEPTH: A Novel Algorithm for Feature Ranking with Application to Genome-Wide Association Studies.

Enes Makalic, Daniel F. Schmidt, John L. Hopper

Proceedings of the AI 2013: Advances in Artificial Intelligence, 2013

2012

Supercomputing enabling exhaustive statistical analysis of genome wide association study data: Preliminary results.

Matthias Reumann, Enes Makalic, Benjamin W. Goudey, Michael Inouye, Adrian Bickerstaffe, Minh Bui, Daniel J. Park, Miroslaw K. Kapuscinski, Daniel F. Schmidt, Zeyu Zhou, Guoqi Q. Qian, Justin Zobel, John Wagner, John L. Hopper

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012