Rasmus Magnusson

This paper uses a deep learning model to identify potential drugs for cardiac hypertrophy by analyzing gene expression data and linking disease signals to known drug targets.

This study uses machine learning on metabolomic data to detect and subtype ketoacidosis in forensic cases with high accuracy.

This study uses metabolomic data and machine learning to predict post-mortem interval with high accuracy, outperforming other models and showing the feasibility of using small datasets.

This paper introduces LiPLike, a method to predict gene regulatory networks with higher certainty by avoiding false positives caused by ambiguous regulatory interactions.

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.

NetAn enhances gene annotation enrichment by integrating network topology, improving identification of disease-related annotations in Multiple Sclerosis.

The paper develops a method to build a comprehensive adipocyte signaling model by iteratively adding phosphosites with confidence levels, achieving high predictive accuracy for inhibitor data.

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.

ComHub predicts hub transcription factors in gene regulatory networks by averaging predictions from multiple network inference methods, showing robust performance on benchmark datasets.

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.

LiPLike improves gene regulatory network predictions by reducing false positives and increasing accuracy compared to existing methods.

LASSIM infers genome-wide gene regulatory networks using mechanistic ODE models, demonstrating its effectiveness in modeling Th2 differentiation with integrated data and siRNA experiments.