Christine Porzelius

发表

Can matching improve the performance of boosting for identifying important genes in observational studies?

Harald Binder, Martin Schumacher, Veronika Reiser, 2012, Computational Statistics.

A general, prediction error‐based criterion for selecting model complexity for high‐dimensional survival models

Harald Binder, Martin Schumacher, Christine Porzelius, 2010, Statistics in medicine.

Hands-on tutorial for parallel computing with R

Manuel J. A. Eugster, Markus Schmidberger, Esmeralda Vicedo, 2011, Comput. Stat..

Graph based fusion of miRNA and mRNA expression data improves clinical outcome prediction in prostate cancer

Tim Beißbarth, Holger Sültmann, Harald Binder, 2011, BMC Bioinformatics.

The benefit of data-based model complexity selection via prediction error curves in time-to-event data

Harald Binder, Martin Schumacher, Christine Porzelius, 2011, Comput. Stat..

Model complexity selection in high-dimensional time-to-event data analysis

Christine Porzelius, C. Porzelius, 2011 .

Bioinformatics Applications Note Parallelized Prediction Error Estimation for Evaluation of High-dimensional Models

Harald Binder, Martin Schumacher, Christine Porzelius, 2022 .

Easier parallel computing in R with snowfall and sfCluster

Guido Schwarzer, Harald Binder, Christine Porzelius, 2009, R J..

Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value

Anne-Laure Boulesteix, Martin Daumer, Christine Porzelius, 2008, Bioinform..

Sparse regression techniques in low-dimensional survival data settings

Harald Binder, Martin Schumacher, Christine Porzelius, 2010, Stat. Comput..

Leveraging external knowledge on molecular interactions in classification methods for risk prediction of patients

Harald Binder, Christine Porzelius, Tim Beissbarth, 2011, Biometrical journal. Biometrische Zeitschrift.