Gerben Menschaert

A systematic assessment of single-cell language model configurations

This study evaluates single-cell language model configurations by systematically testing pre-training strategies and identifies best practices for improving performance.

Translatome and translation dynamics analysis of a RiboCancer cell line panel reveals that leukemia-associated Rps15 mutations rewire translation through codon-specific tRNA accommodation defects.

The paper analyzes translatome and translation dynamics in RiboCancer cell lines to show that Rps15 mutations disrupt tRNA accommodation, affecting translation efficiency and transcriptional regulation in CLL.

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins

The paper identifies 25% of 7,264 non-canonical open reading frames (ncORFs) as producing translated gene products, supported by over 3,000 peptides from 3.8 billion mass spectra.

An expanded reference catalog of translated open reading frames for biomedical research

The paper expands and refines a reference catalog of human non-canonical open reading frames (ncORFs) by incorporating more datasets and introducing a translation signature score framework to prioritize high-quality ncORFs.

Deep learning to decode sites of RNA translation in normal and cancerous tissues

This paper introduces RiboTIE, a transformer model for mapping RNA translation, showing high precision and sensitivity in normal and cancerous tissues.

Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction

The paper introduces Maldi Transformer, a pre-trained model for MALDI-TOF MS data that improves prediction tasks through self-supervised learning.

An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks

This paper develops a dual-branch neural network to predict antimicrobial resistance profiles from MALDI-TOF MS data, improving treatment efficiency and enabling fine-tuning across clinical labs.

RIBO-former: leveraging ribosome profiling information to improve the detection of translated open reading frames.

RIBO-former improves translated open reading frame detection using ribosome profiling data and transformer models, outperforming existing methods and identifying non-canonical ORFs and polycistronic mRNAs.

TIS transformer: Remapping the human proteome using deep learning

The paper introduces TIS Transformer, a deep learning model that improves the mapping of translation initiation sites in the human proteome by analyzing transcript sequences.

A community-driven roadmap to advance research on translated open reading frames detected by Ribo-seq

This paper outlines a community-driven initiative to create a consolidated catalog of human Ribo-seq ORFs, addressing the lack of standardized annotation for these features.

CpG Transformer for imputation of single-cell methylomes

The paper introduces CpG Transformer, a model for imputing single-cell methylomes, achieving state-of-the-art performance on scBS-seq and scRRBS-seq datasets.

Explainable Transformer Models for Functional Genomics in Prokaryotes

This paper introduces transformer-based models for prokaryotic genome annotation and shows that attention heads specialize in identifying DNA binding sites.

Proteomics Standards Initiative Extended FASTA Format (PEFF)

The paper introduces PEFF, a new format for proteomics data that enhances FASTA with metadata for sequence variants and PTMs, enabling better analysis in proteogenomics and top-down proteomics.

DeepRibo: precise gene annotation of prokaryotes using deep learning and ribosome profiling data

DeepRibo uses deep learning and ribosome profiling data to accurately annotate gene expression in prokaryotes, outperforming traditional methods.

The proBAM and proBed standard formats: enabling a seamless integration of genomics and proteomics data.

The paper introduces two new data formats, proBAM and proBed, to integrate proteomics with genomics and transcriptomics data in proteogenomics studies.