Jan Baumbach

Concurrent de novo inference of cell types and gene regulatory networks with NetMap

NetMap jointly infers cell-level gene regulatory networks and clusters cells based on shared regulatory programs, outperforming existing methods on synthetic data and reliably detecting regulons on real data.

Exploring the Comedone Switch through Single-Cell Resolution of Human Acne Lesions

The paper investigates the molecular mechanisms of comedogenesis in human acne using single-cell transcriptomics, identifying a microcomedone-like cell population and supporting the comedone switch hypothesis.

Automated landmark and semilandmark annotation for wing geometric morphometrics in Diptera using deep learning

This paper develops an automated deep learning pipeline for Diptera wing landmark and semilandmark annotation, achieving high accuracy comparable to human observers and outperforming existing methods.

CyChat: a conversational Cytoscape app for no-code, reproducible network analysis

CyChat is a Cytoscape app that translates natural language into reproducible network analysis workflows using a large language model, reducing the need for programming expertise.

Inferring and Evaluating Network Medicine-Based Disease Modules with Nextflow

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

NetMedGPT - A network medicine foundation model for extensive disease mechanism mining and drug repurposing

NetMedGPT is a transformer-based foundation model trained on biomedical knowledge graphs to predict drug-disease associations and support drug repurposing, outperforming specialized models in multiple tasks.

Detection of alternative splicing: deep sequencing or deep learning?

This study evaluates whether sequence-based tools can detect alternative splicing from low-depth RNA-seq data, showing promise for hypothesis generation but limitations in de novo detection.

Aberrant splicing in human cancer has a large-scale functional impact on transcription factors

The paper identifies 2,118 aberrant splicing events affecting 718 transcription factors across 14 cancer types, showing functional perturbations in TFs due to cancer-specific alternative splicing.

Development of an Integrated Single-Cell and Spatial Transcriptomics Atlas of Healthy Human Skin Focusing on the Pilosebaceous Unit

The paper develops an integrated single-cell and spatial transcriptomics atlas of healthy human skin, focusing on the pilosebaceous unit, and identifies previously under-characterized cell types and signaling hubs.

Potentials and limitations in the application of Convolutional Neural Networks for mosquito species identification using wing images

This study develops a CNN-based system for mosquito species identification using wing images, achieving high accuracy and demonstrating practical usability in real-world conditions.

Regulation of the transcriptome, miRNAs, and alternative splicing in a FSGS zebrafish injury model

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

Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions

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

Alternative splicing in mechanically stretched podocytes as a model of glomerular hypertension

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

Systematic Evaluation of Normalization Approaches in Tandem Mass Tag and Label-Free Protein Quantification Data Using PRONE

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

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis

DREAMER identifies molecular mechanisms of adverse drug reactions and disease phenotypes using network-based analysis, revealing key proteins and pathways involved in various conditions.

Transcription factor prediction using protein 3Dsecondary structures

The paper introduces a deep learning method, StrucTFactor, that uses 3D protein structures to predict transcription factors, outperforming existing methods by 17% in Matthews correlation coefficient.

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

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.

RNA sequencing depth guidelines for the study of alternative splicing

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.

Network medicine-based epistasis detection in complex diseases: ready for quantum computing

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.

Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer

The paper identifies prognostic exon-exon interactions in cancer by analyzing RNA-seq and 3D protein structures, showing their association with patient survival.

The power and limits of predicting exon-exon interactions using protein 3D structures

The paper evaluates existing computational methods for predicting exon-exon interactions using protein structures and finds they can achieve moderate accuracy for this task.

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

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.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

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.

Key Proteins for Regeneration in A. mexicanum: Transcriptomic Insights from Aged and Juvenile Limbs

The paper identifies key genes involved in regeneration in axolotl limbs by comparing transcriptomic data from juvenile and aged axolotls.

Alternative splicing impacts microRNA regulation within coding regions

The paper investigates how alternative splicing affects miRNA regulation by analyzing six TCGA cancer datasets, finding that transcripts with miRNA target sites in coding regions better predict miRNA expression.

spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape

spongEffects identifies patient-specific ceRNA modules to provide insights into miRNA regulatory activity using gene expression data alone.

circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging

The paper introduces circRNA-sponging, a pipeline to analyze circRNA expression and miRNA sponging, identifying two types of circRNAs based on binding site ratios.

Systematic analysis of alternative splicing in time course data using Spycone

Spycone analyzes alternative splicing in time course data to detect isoform switches and provides downstream analysis tools.

TF-Prioritizer: a java pipeline to prioritize condition-specific transcription factors

TF-Prioritizer identifies condition-specific transcription factors using multi-modal genomic data and generates an interactive web report.

The Limits of Molecular Signatures for Pancreatic Ductal Adenocarcinoma Subtyping

This study evaluates three PDAC molecular signatures across eight datasets, finding inconsistent subtyping and low classification accuracy, suggesting technical biases and the need for multi-omic approaches.