Decoding heart failure subtypes with neural networks via differential explanation analysis
Ruz Jurado, M.; Rodriguez Morales, D.; Genetzakis, E.; Ardakani, F. B.; Zanders, L.; Fischer, A.; Buettner, F.; Schulz, M. H.; Dimmeler, S.; John, D.
Show abstract
Single-cell transcriptomics offers critical insights into the molecular mechanisms of heart failure with reduced or preserved ejection fraction. However, understanding these mechanisms is hindered by the growing complexity of single-cell data and the difficulty in unmasking meaningful differential genes signatures among heart failure types. Machine learning, particularly deep neural networks, address these challenges by learning transcriptional patterns, reconstructing expression profiles and effectively classifying cells but often lacks interpretability. Recent advances in explainable AI (XAI) offer tools to clarify model decisions. Yet pinpointing differentially regulated genes with these tools remains challenging. In this study, we introduce a novel method to identify differentially explained genes (DXGs) based on importance scores derived from custom-built neural networks. We highlight the superiority of DXGs in identifying heart failure subtypes-specific pathways that provide new insights into different types of heart failure. Offering a robust foundation for future research and therapeutic exploration in expanding transcriptome atlases.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Single-cell genomics improves the discovery of risk variants and genes of Atrial Fibrillation 96%
- Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease 96%
- Uncovering Transcriptional Dark Matter via Gene Annotation Independent Single-Cell RNA Sequencing Analysis 96%
Similar papers in this journal
- Single-cell analysis of chromatin and expression reveals age- and sex-associated alterations in the human heart 97%
- CRISPRi Gene Modulation and All-Optical Electrophysiology in Post-Differentiated Human iPSC-Cardiomyocytes 95%
- Mitochondrial dysfunction drives a neuronal exhaustion phenotype in methylmalonic aciduria 95%
Similar papers in this journal
Similar papers in this journal
- Conserved epigenetic regulatory logic infers genes governing cell identity 97%
- Integrative, high-resolution analysis of single cell gene expression across experimental conditions with PARAFAC2-RISE 95%
- Distinct gene programs underpinning 'disease tolerance' and 'resistance' in influenza virus infection 95%
Similar papers in this journal
- scAlign: a tool for alignment, integration and rare cell identification from scRNA-seq data 95%
- An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs. 95%
- GeneWalk identifies relevant gene functions for a biological context using network representation learning 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.