Tim Kacprowski

The paper benchmarks precision matrix estimation methods for differential co-expression network analysis using simulated datasets and identifies GLassoElnetFast as the most accurate method under certain conditions.

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

TRIPS identifies disease-associated transcription factors by integrating differential expression and network perturbation analysis in case-control data.

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 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 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 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.

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

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.

BoostDiff identifies differential gene regulatory networks between disease and healthy states, outperforming existing methods on simulated and real transcriptomics data.

DICAST benchmarks alternative splicing analysis tools, showing STAR and HISAT2 perform best, and proposes a reporting standard for event detection.

The paper introduces NEASE, a tool for functional analysis of alternative splicing events, showing its ability to identify pathways related to tissue identity and cell type development, and highlight splicing-related biomarkers.

This study identifies 100 loci associated with mitochondrial DNA copy number and finds causal links between mitochondrial function and mortality.

PySCNet is a tool for reconstructing and analyzing gene regulatory networks from single-cell RNA-seq data, offering methods for gene co-expression module detection and gene importance evaluation.

BiCoN identifies patient subgroups and functional gene subnetworks by integrating network constraints with biclustering of omics data.

This study identifies 5,552 blood-cell trait-variant associations across 746,667 individuals from 5 global populations, including 71 novel loci and ancestry-specific variants not found in European ancestry populations.

This study identifies 5,106 new genetic variants associated with blood cell traits using large-scale GWAS, highlighting the polygenic and monogenic basis of hematopoiesis.

sPLINK enables federated GWAS on distributed datasets without data sharing, preserving privacy and accuracy while being robust to heterogeneity.

The paper introduces Scellnetor, a tool to identify systems biology patterns in pseudo-temporal trajectories from scRNA-seq data, applied to hematopoiesis and CD8 T-cell development.

DeepWAS integrates regulatory effect predictions of variants into GWAS to identify regulatory SNPs associated with diseases and traits.

The paper introduces the MS Atlas, a molecular map of brain lesion stages in progressive multiple sclerosis, providing transcriptomic profiles and tools for analysis.

This paper evaluates the causal effect of fibrinogen on coronary heart disease using Mendelian randomization, finding a small effect with multi-variant methods but not with single variant approaches.

This paper introduces a general framework for quantifying similarity between computational biology models and discusses its applications in model management and comparison.