Rasool Saghaleyni

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

The paper integrates spatial transcriptomics data with genetic disease genes to identify tissue structures linked to complex traits and suggests spatially informed drug repurposing.

The paper compares transcriptomic profiles of HEK293 cell lines producing erythropoietin or GFP to identify factors associated with improved erythropoietin production.

The paper uses machine learning and differential expression analysis to identify secretory pathway genes associated with cancer characteristics, finding KIF20A and KIF23 as key genes across multiple cancer types.

The paper analyzes genomic, transcriptomic, and metabolic changes in HEK293 cell lines to understand genetic and compositional differences between adherent and suspension derivatives.