22 citations · 26 across the 4 of their papers we have counts for
4 papers
Improved proteasomal cleavage prediction with positive-unlabeled learning
Emilio Dorigatti, Bernd Bischl, Benjamin Schubert
Accurate in silico modeling of the antigen processing pathway is crucial to enable personalized epitope vaccine design for cancer. An important step of such pathway is the degradat…
What cleaves? Is proteasomal cleavage prediction reaching a ceiling?
Ingo Ziegler, Bolei Ma, Ercong Nie +4
Epitope vaccines are a promising direction to enable precision treatment for cancer, autoimmune diseases, and allergies. Effectively designing such vaccines requires accurate predi…
Uncertainty-aware Pseudo-label Selection for Positive-Unlabeled Learning
Emilio Dorigatti, Jann Goschenhofer, Benjamin Schubert +2
Positive-unlabeled learning (PUL) aims at learning a binary classifier from only positive and unlabeled training data. Even though real-world applications often involve imbalanced…
Population-specific design of de-immunized protein biotherapeutics
Benjamin Schubert, Charlotta Schärfe, Pierre Dönnes +3
Immunogenicity is a major problem during the development of biotherapeutics since it can lead to rapid clearance of the drug and adverse reactions. The challenge for biotherapeutic…