Division Data Science in Biomedicine
Division Data Science in Biomedicine
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The publication titled 'Inference of differential gene regulatory networks using boosted differential trees' now published in Bioinformatics Advances.
On the limits of 16S rRNA gene-based metagenome prediction and functional profiling
Inference of differential gene regulatory networks using boosted differential trees
Alternative splicing impacts microRNA regulation within coding regions
Alternative splicing analysis benchmark with DICAST
Network-based approaches for modeling disease regulation and progression
Systematic analysis of alternative splicing in time course data using Spycone
The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping
Systematic analysis of alternative splicing in time course data using Spycone
sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies
Privacy-Preserving Artificial Intelligence Techniques in Biomedicine
Robust disease module mining via enumeration of diverse prize-collecting Steiner trees
Alternative splicing analysis benchmark with DICAST
The Limits of Molecular Signatures for Pancreatic Ductal Adenocarcinoma Subtyping
Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases
The AIMe registry for artificial intelligence in biomedical research
ASimulatoR: splice-aware RNA-Seq data simulation
Network analysis methods for studying microbial communities: A mini review
BiCoN: Network-constrained biclustering of patients and omics data
DIGGER: exploring the functional role of alternative splicing in protein interactions
sPLINK: A Federated, Privacy-Preserving Tool as a Robust Alternative to Meta-Analysis in Genome-Wide Association Studies
Multi-Omics Analysis in a Network Context (in "Reference Module in Biomedical Sciences")
Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing
EpiGEN: an epistasis simulation pipeline
BiCoN: Network-constrained biclustering of patients and omics data
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