Publications by authors named "Pieter Dewulf"
Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources. While sophisticated alignment algorithms have been pivotal in analyzing biological sequences, deep learning has substantially transformed bioinformatics, addressing sequence, structure, and functional analyses. However, these methods are incredibly data-hungry, compute-intensive, and hard to interpret.
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- * Machine learning models were applied to predict confirmed disability progression after two years, achieving a ROC-AUC score of 0.71, indicating moderate accuracy, while historical disability was found to be a stronger predictor than treatment or relapse history.
- * The research followed strict guidelines and made its coding accessible for others to facilitate future benchmarking in predicting disability progression in MS patients.
Cold-Start Problems in Data-Driven Prediction of Drug-Drug Interaction Effects.
Pharmaceuticals (Basel)
May 2021
Combining drugs, a phenomenon often referred to as polypharmacy, can induce additional adverse effects. The identification of adverse combinations is a key task in pharmacovigilance. In this context, in silico approaches based on machine learning are promising as they can learn from a limited number of combinations to predict for all.
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