Jesper N. Tegner

The paper maps RNA-DNA interactions in 16 human cell types, showing cell-type-specific and dynamic networks that correlate with gene regulation and disease traits.

The paper uses single-cell sequencing to show coordinated immune-cell changes between blood and CSF in early MS, with CSF enrichment of memory T-cells and B-cells and depletion of cytotoxic populations.

This study characterizes gene expression in human growth plates, identifying novel markers and sub-populations of resting zone chondrocytes with features of quiescent stem cells.

This study identifies transcriptional changes in stromal and endothelial cells during MGUS to myeloma progression and treatment response, revealing a tumor-permissive BME signature that is modulated by therapy.

This paper introduces a fractal-dimension framework to quantify self-similarity in chromatin folding using Hi-C data, revealing fractal-like organization and scale-dependent patterns in chromatin structure.

The paper maps RNA-chromatin interactions in human prefrontal cortex, revealing co-regulatory changes linked to brain evolution and disease pathways.

The paper establishes the Saudi Bank of Induced Pluripotent Stem Cells (SBiPSCs) to address underrepresentation of Middle Eastern populations in iPSC resources and supports immunocompatible cell therapies.

The paper introduces tdFBA, a novel method for simulating circadian metabolism across organs, and shows it can recapitulate metabolic oscillations and perturbations in mice and humans.

This study uses spatial transcriptomics to analyze bone marrow in MM mouse models and patients, revealing altered T-cell distribution, neutrophil traps, and inflammatory signaling in MM.

This study evaluates the robustness of epigenetic clocks to common SNPs across diverse ancestries and finds they remain stable despite cSNP-CpG overlaps.

This study evaluates methods for inferring Jacobian matrices from single-cell data to model gene regulatory dynamics, finding that while some methods capture behavioral properties, they fail to reliably capture structural properties.

Unify learns universal cell embeddings across species by integrating RNA expression with protein and general language model embeddings, enabling cross-species comparisons and accurate perturbation response prediction.

GeneSetCluster 2.0 improves gene-set analysis by reducing redundancies, enhancing computational efficiency, and providing accessible tools for both programmers and non-programmers.

The paper identifies transcriptional changes in the avian brain during visual imprinting using single-nucleus RNA sequencing, revealing cell clusters and lncRNAs linked to memory formation.

This study identifies novel RAB13+ endothelial and profibrotic mesenchymal cells in aged human bone marrow using single-cell RNA sequencing and spatial transcriptomics.

The paper identifies molecular dynamics and a new marker, Aspn, for reparative cardiac fibroblasts in mice, pigs, and patients after myocardial infarction.

This study maps epigenetic and transcriptional changes in myeloma progression from precursor to advanced stages, identifying key transcription factors involved in disease development.

This paper introduces a method to predict perturbation responses in unseen cell types using cell-type-specific graphs, showing improved performance over existing methods.

This paper introduces a denoising diffusion model to predict and interpolate spatial transcriptomics data, enabling generative modeling of tissue slices.

This study establishes an HLA-based iPSC banking system for the Saudi population, demonstrating that a small number of iPSC lines can cover a significant portion of the population's HLA haplotypes.

The paper predicts missing links in the human Protein-Protein Interaction network using a transformer model that combines semantic and structural features.

This study evaluates how single-cell foundation models handle imbalanced cell-type data and finds that scGPT and scBERT outperform Geneformer, with random oversampling improving performance.

This study uses a variational autoencoder trained on healthy human RNA-seq data to predict disease gene expression changes and identify drug targets through latent space arithmetic.

LEP-AD uses a Transformer protein language model to predict drug-target interactions, achieving state-of-the-art results across multiple datasets.

scAEGAN unifies single-cell genomics data by adversarial learning of latent space correspondences, outperforming existing methods in integration and prediction tasks.

The paper identifies gene regulatory networks in human GM-CSF-secreting CD4 T cells using ATAC-seq and RNA-seq data, linking them to autoimmune disease.

The paper introduces a conceptual framework and bioinformatics pipeline for multi-omics data integration, validated on TCGA datasets to enhance feature identification compared to single-omics analysis.

DeepViral improves prediction of virus-host protein interactions by incorporating disease phenotypes and protein sequences.

The paper predicts human transcriptomic profiles from transcription factor expression using a deep neural network, finding that 1,600 TFs explain >95% of variance in 25,000 genes.

This paper introduces LIBRA, a neural network framework for integrating and predicting across paired single-cell multi-omics data, showing improved clustering resolution and predictive power.