Anna Cichońska

The paper proposes a two-stage machine learning method that combines single-dose and dose-response data to improve compound-kinase activity prediction, achieving high accuracy and hit rates in experimental validation.

The paper performs metabolite GWAS on UK Biobank data to investigate pleiotropy and genetic correlation, finding pathway-relevant genes and contrasting variant effects.

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.

The paper introduces a multivariate GWAS framework that identifies functional variants and disease associations by transforming multivariate results into univariate phenotypes for follow-up analysis.