Henry Emanuel Webel

The paper applies variational autoencoders to integrate biomedical data, explores model dynamics, and evaluates methods using synthetic and real datasets.

This paper introduces a method using protein language models to construct pangenomes, achieving better cluster quality and functional consistency than existing tools.

Mycol provides an accessible, open-source platform for microscopy image analysis using machine learning, enabling non-specialists to perform segmentation, classification, and quality control with minimal setup.

The paper introduces growthcurves, a Python package and web tools for analyzing biological growth curves with human-in-the-loop workflows, providing automated fitting and interactive inspection.

ABaCo integrates metagenomic data from multiple studies, corrects technical heterogeneity, and outperforms existing methods while preserving biological signals.

pmultiqc generates metadata-aware QC reports for proteomics data using standardized metrics and integrates with existing workflows and cloud services.

quantms-rescoring improves proteomics analysis by integrating multiple search engines and machine learning, increasing identified spectra and quantifying more phosphorylated peptides and modification sites.

ProteoBench provides a centralized platform for comparing proteomics data analysis workflows, enabling benchmarking and community collaboration.

The paper applies a variational autoencoder to multi-omics data from the UK Biobank to assess disease risk prediction and the impact of preprocessing on model performance.

ibaqpy computes iBAQ values for proteomics data using SDRF metadata, improving scalability and reproducibility in large-scale experiments.

VueGen automates report generation from bioinformatics outputs, enabling non-experts to create reports with minimal coding experience.

ProteusAI is an open-source platform that streamlines ML-guided protein design and engineering, improving proteins and enzymes efficiently within the DBTL cycle.

The paper identifies non-linear genetic regulation of plasma proteins using a deep learning method, finding dominance and epistasis effects in large cohorts.

This paper introduces a deep learning method for imputing missing values in label-free quantitative proteomics data and shows its effectiveness in identifying differentially abundant proteins in alcohol-related liver disease.

The paper evaluates consensus spectrum generation methods in proteomics, finding BEST and BIN to be the most reliable for peptide identification.

The paper introduces a standardized metadata format for proteomics datasets to improve reusability and integration across public repositories.