10 citations · 12 across the 5 of their papers we have counts for
5 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…
Joint Debiased Representation and Image Clustering Learning with Self-Supervision
Shunjie-Fabian Zheng, JaeEun Nam, Emilio Dorigatti +3
Contrastive learning is among the most successful methods for visual representation learning, and its performance can be further improved by jointly performing clustering on the le…
Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision
Emilio Dorigatti, Jonas Schweisthal, Bernd Bischl +1
Learning from positive and unlabeled (PU) data is a setting where the learner only has access to positive and unlabeled samples while having no information on negative examples. Su…
Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
Cornelius Fritz, Emilio Dorigatti, David Rügamer
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease…