Steffen Albrecht

Spiking neural networks provide accurate, efficient and robust models for whisker stimulus classification and allow for inter-individual generalization

The paper benchmarks spiking neural networks for whisker stimulus classification in mice, showing their accuracy, efficiency, and ability to generalize across individuals.

Evaluating Genetic Regulators of MicroRNAs Using Machine Learning Models

This study uses machine learning to predict miRNA expression and identify genetic regulators, revealing miRNA-gene regulatory relationships and pathway-specific associations.

Interpretable machine learning models for single-cell ChIP-seq imputation

SIMPA imputes missing regions in scChIP-seq data using machine learning models trained on bulk data, improving clustering and pathway gene identification while enabling model interpretation.

Quality-preserving low-cost probabilistic 3D denoising with applications to Computed Tomography

The paper introduces a probabilistic Mumford-Shah denoising model that effectively reduces noise in 3D images and 2D videos while maintaining quality and computational efficiency.

m6A RNA methylation regulates promoter proximal pausing of RNA Polymerase II

This study shows that m6A RNA methylation regulates RNAP II promoter-proximal pausing, affecting gene expression and transcription.

Automated quality control of next generation sequencing data using machine learning

This paper develops machine learning models for automated quality control of next generation sequencing data, validated on internal and external datasets.

Computational identification of cell-specific variable regions in ChIP-seq data

The paper develops a method to identify cell-specific variable regions in ChIP-seq data by integrating replicated experiments, finding regions with variable occupancy that may have functional significance.

TAF-ChIP: An ultra-low input approach for genome wide chromatin immunoprecipitation assay

TAF-ChIP enables high-quality ChIP-Seq results from low cell numbers, matching conventional ChIP-Seq datasets in epigenetic profiles.