A novel approach to the clustering of microarray data via nonparametric density estimation. - Riccardo De Bin; Davide Risso (BMC Bioinform., 2011)

Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost. - Riccardo De Bin (Comput. Stat., 2016)

IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data. - Anne-Laure Boulesteix; Riccardo De Bin; Xiaoyu Jiang; Mathias Fuchs (Comput. Math. Methods Medicine, 2017)

Detection of influential points as a byproduct of resampling-based variable selection procedures. - Riccardo De Bin; Anne-Laure Boulesteix; Willi Sauerbrei (Comput. Stat. Data Anal., 2017)

On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models. - Heidi Seibold; Christoph Bernau; Anne-Laure Boulesteix; Riccardo De Bin (Comput. Stat., 2018)

Combining clinical and molecular data in regression prediction models: insights from a simulation study. - Riccardo De Bin; Anne-Laure Boulesteix; Axel Benner; Natalia Becker; Willi Sauerbrei (Briefings Bioinform., 2020)

Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models. - Shaima Belhechmi; Riccardo De Bin; Federico Rotolo; Stefan Michiels (BMC Bioinform., 2020)

Simple statistical models and sequential deep learning for Lithium-ion batteries degradation under dynamic conditions: Fractional Polynomials vs Neural Networks. - Clara B. Salucci; Azzeddine Bakdi; Ingrid Kristine Glad; Erik Vanem; Riccardo De Bin (CoRR, 2021)

A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data. - Alise Danielle Midtfjord; Riccardo De Bin; Arne Bang Huseby (CoRR, 2021)

Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicine. - Shaima Belhechmi; Gwénaël Le Teuff; Riccardo De Bin; Federico Rotolo; Stefan Michiels (BMC Bioinform., 2023)

GPTreeO: An R package for continual regression with dividing local Gaussian processes. - Timo Braun; Anders Kvellestad; Riccardo De Bin (CoRR, 2024)

Automatic piecewise linear regression. - Mathias von Ottenbreit; Riccardo De Bin (Comput. Stat., 2024)