2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Improving Performance in Classification Tasks with LCEN and the Weighted Focal Differentiable MCC Loss
Pedro Seber, Richard D. Braatz
The LASSO-Clip-EN (LCEN) algorithm was previously introduced for nonlinear, interpretable feature selection and machine learning. However, its design and use was limited to regress…
cs.LG2024★ 2 cited
Predicting O-GlcNAcylation Sites in Mammalian Proteins with Transformers and RNNs Trained with a New Loss Function
Pedro Seber
O-GlcNAcylation, a subtype of glycosylation, has the potential to be an important target for therapeutics, but methods to reliably predict O-GlcNAcylation sites had not been availa…
cs.LG2024★ 1 cited
LCEN: A Nonlinear, Interpretable Feature Selection and Machine Learning Algorithm
Pedro Seber, Richard D. Braatz
Interpretable models can have advantages over black-box models, and interpretability is essential for the application of machine learning in critical settings, such as aviation or…