Tapio Pahikkala

The paper develops a single-cell amine analysis method using isobaric multiplexing and mass spectrometry, achieving absolute quantification of metabolites and modest cell type separation.

This study compares three embedding aggregation strategies for drug-target interaction prediction using benchmark datasets, showing when each strategy is most effective.

This study evaluates the use of resting state EEG for Parkinson's disease classification, finding that fewer electrodes and open-eye conditions improve accuracy.

The paper introduces comboLTR, a method for predicting drug combination effects using latent tensor reconstruction, outperforming existing methods in speed and accuracy.

comboFM predicts drug combination effects using machine learning, achieving high accuracy in cancer cell line screening and validating novel synergies.

This study provides a molecular atlas of postnatal mouse heart development by integrating transcriptomics, proteomics, and metabolomics data to identify metabolism-related mechanisms linked to the loss of cardiac regeneration.

The paper introduces algebraic shortcuts for leave-one-out cross-validation to evaluate kernel-based network inference methods efficiently.