Marcel J. T. Reinders

The paper introduces treeArches, a framework for building and extending cell-type hierarchies by mapping new datasets to reference atlases and harmonizing annotations across studies.

This paper introduces TACTiCS, a model that matches cell types across species using protein embeddings and transfer learning, outperforming existing methods.

The paper uses scRNA-seq data to identify gene expression rewiring in Alzheimer's Disease linked to risk variants by analyzing differential correlations across cell types.

Percolate combines gene expression with other genomic data to create robust DNA-based predictors of drug response, outperforming individual data types.

scTopoGAN aligns single-cell datasets with non-overlapping cells or features using topology-guided GANs, outperforming existing methods in unsupervised settings.

The paper identifies rare damaging variants in ATP8B4 and ABCA1 as novel risk factors for Alzheimer's Disease using exome sequencing data from 32,558 individuals.

This paper introduces a model that predicts cell population-specific gene expression from genomic sequence, outperforming tissue-specific models and enabling GWAS variant prioritization and motif learning.

This paper improves methylation-based cell type deconvolution of cfDNA by introducing CelFEER, which achieves higher accuracy in estimating cell type proportions and detects significant differences in skeletal muscle cfDNA between ALS patients and controls.

This paper introduces Reafect, a computational method that integrates metabolomics and whole exome sequencing to prioritize disease-causing genes in inborn errors of metabolism, showing improved accuracy in identifying causative variants.

This paper identifies age and Alzheimer's disease-associated somatic mutations in excitatory neurons using WGS and scRNA-seq data from the ROSMAP project.

The paper shows that binarized gene expression in scRNA-seq is as informative as count-based expression and scales better to larger datasets.

This paper introduces Sobolev Alignment, a method to identify shared biological processes between cancer cell lines and tumors using deep generative models and kernel methods, showing its effectiveness on synthetic and real datasets.

This study evaluates metabolic predictors as alternatives to clinical variables in TWAS, finding they yield similar results and can complement incomplete phenotypic data.

scMoC integrates scRNA-seq and scATAC-seq data to identify biologically meaningful cell clusters through RNA-guided imputation and cross-modal clustering.

This study examines how Alzheimer's disease-associated genetic variants influence longevity by analyzing their effects in centenarians and replicating findings in a parental-longevity GWAS.

The paper develops TRANSACT, a transfer learning framework for predicting clinical drug response, outperforming existing methods on multiple datasets.

The paper identifies gene expression patterns linked to cortical thickness changes in Parkinson's disease using MRI and transcriptomic data, revealing pathways related to cellular maintenance mechanisms.

SIRV infers spatial RNA velocities at single-cell resolution by integrating spatial transcriptomics with scRNA-seq data, revealing spatial differentiation patterns in developing tissues.

This paper introduces scHPL, a hierarchical progressive learning method for single-cell data that continuously updates cell identity classifications across multiple datasets while preserving original annotations.

snpXplorer is a web tool that explores SNP associations, annotates SNP sets, and compares GWAS results across traits.

The paper introduces differential dropout analysis to detect biological variation in single-cell RNA sequencing data and shows it outperforms count-based methods.

The paper demonstrates that using universal contextualized protein embeddings from SeqVec allows effective cross-species molecular function prediction, especially for understudied species.

The paper develops a normalization method for untargeted metabolomics data to reduce batch effects and improve detection of metabolic biomarkers using out-of-batch controls.

Cytosplore-Transcriptomics provides a scalable interactive framework for analyzing scRNA-seq data, enabling visualization and downstream analysis of large datasets.

The paper evaluates how incomplete PPI networks contribute to functional annotation across species, showing improved performance with additional data sources like text mining.

The paper identifies immune response and endocytosis pathways as associated with resilience against Alzheimer's Disease using polygenic risk scores across multiple cohorts.

The paper identifies functionally important variants in conserved non-protein-coding regions of the chicken genome using a variant effect prediction tool called chCADD.

SCHNEL is a scalable clustering tool for high-dimensional single-cell data that outperforms existing methods on cytometry datasets and handles large datasets efficiently.

SpaGE integrates spatial and scRNA-seq data to predict whole-transcriptome spatial gene expressions, outperforming existing methods and identifying new spatial gene patterns.