Abolfazl Doostparast Torshizi

This study uses machine learning to model proteogenomic data and identifies plasma biomarkers for early lung cancer detection, achieving AUCs of 0.78-0.88 in predicting diagnosis up to 9 years before symptoms.

CellR imputes cell type-specific expression profiles and cellular compositions from bulk RNA-Seq data in brain disorders, showing improved accuracy and stability compared to existing methods.

The paper introduces a method to integrate multi-omics data for predicting schizophrenia risk genes by modeling proteogenomic signal diffusion across cell types and tissues.

The paper deconvolves transcriptional networks in schizophrenia to identify TCF4 as a master regulator potentially dysregulated in the disease.

This study identifies PPP1R3F as a master regulator in ASDs using network deconvolution and RNA-Seq data from cerebellum samples.