23 citations · 30 across the 6 of their papers we have counts for
6 papers
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks
Regina Ibragimova, Dimitrios Iliadis, Willem Waegeman
Recently, machine learning (ML) has gained popularity in the early stages of drug discovery. This trend is unsurprising given the increasing volume of relevant experimental data an…
Assessment of Uncertainty Quantification in Universal Differential Equations
Nina Schmid, David Fernandes del Pozo, Willem Waegeman +1
Scientific Machine Learning is a new class of approaches that integrate physical knowledge and mechanistic models with data-driven techniques for uncovering governing equations of…
The out-of-sample : estimation and inference
Stijn Hawinkel, Willem Waegeman, Steven Maere
Out-of-sample prediction is the acid test of predictive models, yet an independent test dataset is often not available for assessment of the prediction error. For this reason, out-…
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
It is well known that accurate probabilistic predictors can be trained through empirical risk minimisation with proper scoring rules as loss functions. While such learners capture…
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Tapio Pahikkala, Michiel Stock, Antti Airola +3
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic pre…
Identification of functionally related enzymes by learning-to-rank methods
Michiel Stock, Thomas Fober, Eyke Hüllermeier +6
Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually de…