Marieke L. Kuijjer

SiSaNA provides a command line interface for single-sample regulatory network analysis without requiring programming experience.

The paper benchmarks clustering strategies for patient-specific gene regulatory networks across 9,778 cancer samples, finding that standard deviation-based feature selection with k-means performs best.

The paper identifies a pseudogene co-expression signature that predicts survival in pediatric B-ALL using single-sample networks and LASSO feature selection.

This paper analyzes single-sample network inference methods, identifying trade-offs between accuracy and specificity and suggesting opportunities for methodological synergy.

The paper analyzes gene regulatory networks across 9,000 tumors to identify recurrent PD-1 pathway dysregulation linked to patient outcomes.

This study integrates multi-omics data with gene regulatory networks to improve survival predictions in ten cancer types, showing improved associations with patient survival when GRNs are included.

SPONGE generates prior gene regulatory and protein-protein interaction networks using biological databases to enhance network inference tools like PANDA.

The paper reconstructs patient-specific gene co-expression networks in lung adenocarcinoma to identify subtypes and survival predictive biomarkers.

This study analyzes the genomic changes in osteosarcoma over time using WGS, revealing increasing genomic complexity and variable evolutionary patterns.

This paper introduces SCORPION, a tool for reconstructing gene regulatory networks from single-cell RNA-seq data, showing it outperforms existing methods and detects consistent regulatory differences in colorectal cancer.

The paper introduces SNAIL, a normalization method that reduces false-positive co-expression associations in RNA-sequencing data, improving downstream network analysis.

The paper develops a computational framework to identify regulatory heterogeneity in leiomyosarcoma by analyzing gene regulatory networks and validates findings in independent datasets.

The paper introduces immuneREF, a method to quantify immune repertoire similarity across health and disease, finding that healthy and diseased repertoires are more similar than previously thought.

The paper introduces netZoo, an open-source software suite for analyzing biological networks, demonstrating its use on cancer cell line data to identify drug resistance and cancer development factors.

The paper develops a computational tool to predict drug combinations for TNBC by integrating transcriptional signatures with drug response data.

gpuZoo accelerates gene regulatory network inference by using GPU acceleration, achieving faster and cheaper runtime compared to CPU-based methods.

immuneML provides an open-source ecosystem for reproducible and interoperable machine learning analysis of adaptive immune receptor repertoires, demonstrating its utility through benchmarking and novel methods.

The paper describes applying LIONESS to reconstruct sample-specific networks using different algorithms and data types, discussing preprocessing impacts and analysis methods.

GRAND is a database of gene regulatory network models across human conditions, enabling comparison between biological states and drug response prediction.

The paper identifies PD1 signaling regulation as a prognostic marker in glioblastoma using gene regulatory network analysis across two cohorts.

The paper introduces rPanglaoDB, an R package for downloading and merging labeled single-cell RNA-seq data from PanglaoDB, and demonstrates its use in characterizing fibrocytes through data integration.

The paper integrates genomic, epigenomic, and transcriptomic data to model enhancer-driven gene regulatory networks in maize tissues, identifying tissue-specific transcription factor families and transposable element contributions.

PUMA integrates miRNA target predictions with gene expression data to identify tissue-specific miRNA regulatory interactions and their disease relevance.

lionessR provides an R implementation of LIONESS for single-sample network reconstruction using any network method that outputs a weighted adjacency matrix, demonstrating its use on a bone cancer dataset.

The paper identifies sex-linked differences in colon cancer drug metabolism via gene regulatory network analysis, showing improved survival in females with higher pathway targeting.

This study identifies cancer subtypes using somatic mutation data from The Cancer Genome Atlas, finding associations with prognostic outcomes and treatment options.

The paper develops a preprocessing and normalization pipeline for large-scale RNA-Seq data, addressing batch and tissue effects in heterogeneous datasets.

This study analyzes gene regulation across 38 tissues to show that transcription factor-target connections are more tissue-specific than genes, and that tissue-specific genes are not highly targeted in their networks but occupy bottleneck positions.

The paper compares gene expression and regulatory patterns between cell lines and their source tissues, finding reduced expression levels and altered transcription factor regulation in cell lines.