Hossein Sharifi-Noghabi

This paper provides guidelines for developing machine learning models to predict drug sensitivity using cell line-based pharmacogenomics data, emphasizing proper training and validation practices.

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

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

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

This study introduces DGS, a method combining denoising autoencoders and transfer learning to predict prostate cancer metastasis using both labelled and unlabelled gene expression data, achieving higher accuracy than existing signatures.