Olga I. Zolotareva

The paper evaluates normalization methods in proteomics data, finding that some methods perform better than others and develops a tool to assess their impact.

Velodrome improves drug response prediction by generalizing across data distributions using labeled and unlabeled gene expression data from diverse sources.

DESMOND identifies differentially expressed gene modules in heterogeneous diseases by integrating gene expression and protein interaction data, improving reproducibility and biological relevance.

AITL improves drug response prediction by addressing input and output space discrepancies between pre-clinical and clinical pharmacogenomics datasets.

This paper introduces BDKANN, a neural network using biological knowledge to predict cancer drug response from gene expression data, achieving low error and interpretable results.

MOLI integrates multi-omics data using deep neural networks to predict drug response, achieving higher accuracy than single-omics and early integration methods.