Fatemeh Behjati Ardakani

Harnessing machine learning models for epigenome to transcriptome association studies

The paper benchmarks machine learning models for predicting gene expression from epigenomic data using the IHEC EpiATLAS dataset and identifies genes linked to B-cell leukemia.

EpiATLAS - a reference for human epigenomic research

EpiATLAS provides a comprehensive collection of human reference epigenomes to advance epigenomic research and understanding of cellular function and disease.

Predicting gene-specific regulation with transcriptomic andepigenetic single-cell data

The paper introduces MetaFR, a method that links scATAC-seq open-chromatin variation to scRNA-seq gene expression, showing improved performance over existing methods.

Cell type-specific epigenetic regulatory circuitry of coronary artery disease loci

The paper identifies 1,580 cell type-specific genes and non-coding RNAs associated with coronary artery disease by integrating genetic and epigenetic data from multiple cell types.

Decoding heart failure subtypes with neural networks via differential explanation analysis

This study identifies differentially explained genes in heart failure subtypes using neural networks and XAI tools to improve understanding of molecular mechanisms.

Metabolic alterations drive inflammatory phenotypes in CHIP-associated heart failure

This study shows that DNMT3A mutations in CHIP lead to metabolic changes in macrophages that drive inflammation, and targeting these changes can improve heart failure outcomes.

CVD-associated SNPs with regulatory potential drive pathologic non-coding RNA expression

The paper identifies regulatory SNPs linked to CVDs and shows their impact on non-coding RNA expression and cardiac function in disease models.

The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data

The paper adapts the ABC model for enhancer-gene assignment, improves accuracy by incorporating TF binding and single-cell data, and introduces the STARE pipeline.

Integrative analysis of single cell expression data reveals distinct regulatory states in bidirectional promoters

The paper integrates single cell RNA-seq with bulk epigenomic data to identify distinct regulatory states in bidirectional promoters and their associations with structural features and TF binding.

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

This paper introduces TEPIC, a method that combines open-chromatin data with TF binding affinities to predict gene expression, achieving results comparable to TF ChIP-seq.