Andreas Dengel

Explainable Artificial Intelligence for Cross-Dataset Generalizable Biomarker Discovery in Cardiovascular diseases (CVDs)

This study harmonizes cross-platform gene expression data using three techniques, evaluates ML/DL classifiers, and applies XAI methods to identify interpretable biomarkers for cardiovascular diseases.

An Explainable Knowledge Graph-Driven Approach to Decipher the Link Between Brain Disorders and the Gut Microbiome

This paper introduces GNN-GBA, a graph neural network that identifies mechanistic pathways linking gut microbes to brain disorders using a large biomedical knowledge graph.

Artificial Intelligence Powered Biomarker Discovery: A Large-Scale Analysis of 236 Studies Across 19 Therapeutic Areas and 147 Diseases

This study analyzes 236 AI-driven biomarker discovery studies across 147 diseases and 19 therapeutic areas to identify trends and gaps in datasets, methods, and disease-specific biomarkers.

BIND: Large-Scale Biological Interaction Network Discovery through Knowledge Graph-Driven Machine Learning

BIND predicts biological interactions using knowledge graph embeddings and machine learning, achieving high F1 scores and identifying novel drug-phenotype interactions supported by literature.

Automating AI Discovery for Biomedicine Through Knowledge Graphs And LLM Agents

This paper introduces a framework combining knowledge graphs and LLM agents to automate biomedical discovery by identifying relevant pathways, designing AI predictors, and proposing wet-lab experiments.