Robert B. Faryabi

Lineage-determining transcription factors constrain cohesin to drive multi-enhancer oncogene regulation

The paper shows lineage-determining transcription factors position enhancers and promoters to enable multi-enhancer regulation of oncogenes in lymphoma and T-cell leukemia.

OLIVE Provides Rapid Visualization and Analysis of Chromatin Tracing Experiments

OLIVE provides a web-based tool for visualizing and analyzing chromatin tracing data, enabling interactive exploration and quantitative analysis of three-dimensional chromatin structures.

Single-Allele Chromatin Tracing Reveals Genomic Clustering of Paralogous Transcription Factors as a Mechanism for Developmental Robustness in T Cells

The paper uses single-allele chromatin tracing to show that genomic clustering of paralogous transcription factors enables developmental robustness in T cells by allowing compensatory interactions when regulatory elements are lost.

Azurify integrates cancer genomics with machine learning to classify the clinical significance of somatic variants

Azurify classifies somatic variants' clinical significance using machine learning and integrates with clinical data, achieving high accuracy in external validation.

Enhancer-promoter hubs organize transcriptional networks promoting oncogenesis and drug resistance

This study identifies enhancer-promoter hubs in cancer types and shows their role in oncogenesis and drug resistance through topological and transcriptional changes.

Aberrant TNF signaling in pancreatic lymph nodes of patients with Type 1 Diabetes

This study identifies heightened TNF pathway activity and chromatin remodeling in pancreatic lymph node CD4+ T cells from T1D patients, supporting the use of TNF inhibitors in clinical trials.

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics data, showing improved performance over existing methods in diabetic tissue analysis.

Modeling Type 1 Diabetes progression from single-cell transcriptomic measurements in human islets

This study uses single-cell transcriptomics and machine learning to predict T1D development in autoantibody-positive donors and identifies shared gene signatures across cell types.

Computational workflow and interactive analysis of single-cell expression profiling of islets generated by the Human Pancreas Analysis Program

The paper presents a computational workflow and interactive tool for analyzing single-cell RNA-seq data from pancreatic islets of diabetic and non-diabetic donors.

Multiomics single-cell analysis of human pancreatic islets reveals novel cellular states in health and type 1 diabetes

TooManyCells identifies and visualizes relationships of single-cell clades

TooManyCells identifies and visualizes cell clades using graph-based tools, outperforming existing methods in detecting rare populations in single-cell RNA-seq data.

Differential Integration of Transcriptome and Proteome Identifies Pan-cancer Prognostic Biomarkers

This paper introduces inteGREAT, a method for integrating transcriptome and proteome data to identify prognostic biomarkers across cancers, showing high performance in synthetic and real data analyses.