Markus List

The paper introduces TACTIC, a tool that combines denoising and clustering to improve 16S rRNA diversity estimates by reducing inflation caused by within-species sequence variation.

POME provides graph-based embeddings for partially observed mixed-type biomedical data, achieving state-of-the-art imputation and enabling downstream analyses like subgroup discovery and predictive modeling.

Inferring tested-but-undetected pairs from prey lists of 5,530 published interaction screens yields 97 million tested human protein pairs and 18,982,508 non-interactions (≥3 tests) for calibrating screens and predictors.

Rectangle provides a scalable deconvolution framework for bulk RNA-seq using single-cell data, achieving high accuracy and robustness across diverse benchmarks.

This paper introduces a Nextflow pipeline to evaluate disease module detection methods, showing variability in results due to method, network, and seed differences.

This study identifies SATB1 as a regulator of JAK-STAT signaling and cytokines in human Treg and Tconv cells, showing its potential as a target for CAR T cell therapy.

The paper identifies somatic DNA methylation heterogeneity in Arabidopsis leaves and links it to transgenerational epimutation hotspots.

The paper identifies circular RNAs that change in response to anti-hormonal treatments in mouse models, suggesting potential as non-invasive biomarkers for therapeutic response.

GNExT is a web-based platform that integrates GWAS data with systems medicine approaches to identify potential drug repurposing candidates and enable large-scale network medicine investigations.

CAPRINI-M curates cardiac protein interactions using AI, integrating literature and structural data to improve PPI resource quality and performance.

This study highlights issues with circRNA detection in poly(A)-enriched RNA-seq data, showing it leads to false positives and recommends total RNA-seq with rRNA depletion for accurate profiling.

This paper benchmarks second-generation single-cell-informed deconvolution tools for bulk RNA-seq, revealing performance differences based on data characteristics and providing an ecosystem called omnideconv.

The paper combines network inference and motif enrichment to identify transcription factors controlling stress responses in barley under drought and Fusarium head blight.

The paper constructs co-expression networks using gene and isoform data from individuals with and without stress-related psychiatric disorders, revealing distinct network structures and regulatory rewiring.

This study investigates alternative splicing and miRNA regulation in a zebrafish FSGS model, identifying key splicing events and miRNAs associated with disease progression.

The paper benchmarks differential transcript usage analysis tools across static and dynamic conditions, finding optimal tools for different data types and experimental settings.

This paper refines the Tangram spatial mapping tool to improve consistency and reliability in mapping single-cell RNA-seq data to spatial transcriptomics data.

The paper investigates alternative splicing in mechanically stretched podocytes, identifying splicing events in Shroom3 and Myl6 that may contribute to hypertensive nephropathy.

This paper evaluates drug response prediction models and finds that deep learning models perform similarly to simple baselines, highlighting issues in current model evaluation practices.

This study shows that sequence-based PPI prediction models using ESM-2 embeddings plateau at 0.65 accuracy, suggesting structural information may be needed for better performance.

The paper evaluates normalization methods in proteomics data, finding that some methods perform better than others and develops a tool to assess their impact.

The paper introduces deconvMe, an R package for DNA methylation-based cell-type deconvolution, and compares its results to gene expression and experimental data.

This study shows that power-law distributions in PPI networks can arise from study and technical biases, not biological properties.

DysRegNet infers patient-specific gene-regulatory networks from bulk RNA-seq data, accounting for confounders and scaling to large sample sizes, showing age-specific bias in breast cancer.

The paper provides sequencing depth guidelines for comprehensive alternative splicing analysis across tissues, showing that current depths are insufficient and recommending deeper sequencing or long-read technologies.

The paper re-evaluates epigenetic control of alternative splicing using EpiATLAS data, confirming associations between DNAm and H3K36me3 with exon inclusion and highlighting gene expression biases and epigenetic prediction of PSI values.

The paper introduces NeEDL, a network medicine-based method for detecting higher-order epistatic interactions in complex diseases, showing improved statistical significance and applicability across eight diseases.

This study benchmarks differential expression methods for single-cell data, finding no performance advantage for single-cell-specific methods over pseudobulk approaches like DESeq2.

This study evaluates 16S rRNA gene-based functional prediction tools using simulated and metagenomic data, finding they lack sensitivity to detect health-related microbiome functional changes.

The paper uses bulk RNA-seq to analyze immune responses to SARS-CoV-2 variants, showing that 10 million reads per sample can estimate immune cell composition and BCR/TCR sequences.