Jesper N. Tegner

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.

scMomentum infers cell-type-specific regulatory networks and energy landscapes from single-cell transcriptomic data without needing temporal or perturbation experiments.

The paper develops a mathematical model combining mRNA splice variants and time-delay to predict protein abundance from RNA-Seq, improving biomarker discovery in immune diseases.

The paper uses a deep auto-encoder to discover disease-related gene modules from transcriptional data without relying on prior biological networks, finding significant enrichment of GWAS genes in the third layer.

The paper introduces a multitask learning module for Transformers to analyze large-scale single-cell transcriptomes, achieving better performance than autoencoder and PCA on the Mouse Brain Atlas.

The paper introduces a method to build gene regulatory networks from scATAC-seq and scRNA-seq data using linked self-organizing maps, demonstrating its application in mouse pre-B cell differentiation.

The paper presents STATegra, a multi-omics dataset of mouse B-cell differentiation combining 10 omics technologies to study dynamic cellular processes.

The paper introduces Nodewalk, a highly sensitive method to analyze chromatin networks, revealing that enhancers around MYC operate in a stochastic, mutually exclusive manner, impacting cancer cell fitness.

The paper introduces a new framework for causal discovery and system reprogramming using algorithmic information theory, applied to biological systems like E. coli and cell differentiation networks.

The paper applies causal discovery methods to mass cytometry data of human immune cells to predict causal relationships between signaling proteins and their responses to perturbations.