Johan Trygg

This paper introduces a probabilistic framework using Bayesian inference to improve metabolic rate estimation from sparse concentration data in bioprocesses.

The paper develops a hybrid modeling framework for predicting CHO cell culture digital twins by integrating ODE models, metabolic constraints, and machine learning trained on Bayesian-estimated metabolic rates.

This paper introduces LCTP, a workflow for analyzing live-cell time series data to improve mode of action classification by capturing temporal phenotypes across compounds and cell lines.

The paper identifies a conserved set of transcription factors in human CD4+ T cell subtypes and shows subtype-specific rewiring of gene regulatory networks during activation and differentiation.

The paper identifies a JUNB+ epigenetic state in CD4 T cells associated with proliferation, exhaustion, and memory formation, and develops a new machine learning tool to analyze its regulation.

This paper introduces two deep learning predictors for YY1-mediated chromatin loop prediction, showing improved performance across cell lines and benchmark datasets.

The paper introduces a kmerRP2vec feature representation method combined with CNN and RNN to predict miRNA subcellular locations, outperforming existing methods on a public dataset.

The paper reports a whole-genome sequenced control population from northern Sweden, revealing genetic substructure and improving variant imputation accuracy.

doepipeline optimizes bioinformatic software parameters using experimental design methods, improving outcomes compared to default settings in four use cases.