Pauline Hermans

This study evaluates computational models for predicting pharmacogenomic variant effects, finding that StructureDCA outperforms other methods in distinguishing functional variants.

The paper investigates whether implicit motor adaptation in young and older adults is similarly modulated by task outcomes, finding no consistent age effect in the motor domain but effects in the cognitive domain.

The paper links somatic mutations in cancer to DNA electronic properties by showing anticorrelations between mutation frequencies and vertical ionization potential (vIP) values of nucleobase motifs.

The paper introduces a simple model using residue conservation and solvent accessibility to predict mutational effects, showing it performs as well as complex deep learning models.

The paper evaluates variant effect predictions for ARSA gene variants, finding that a bootcamp-trained model outperformed others and that deep learning slightly improved performance over simpler methods.

This study replicates and refutes previous findings that older adults show reduced implicit motor learning and spontaneous recovery, showing no age-related differences in these processes.

The paper evaluates how evolutionary models predict protein stability using deep mutational scanning data and finds that simple models perform as well as complex ones when combined with structural features.

FiTMuSiC predicts protein fitness effects using structural and coevolutionary data, achieving high accuracy and providing mechanistic insights through functional and structural scores.