Sebastian Lobentanzer

This paper introduces Karenina, a framework that evaluates biomedical AI using expert knowledge across multiple dimensions beyond traditional scoring.

CROssBARv2 integrates heterogeneous biomedical data into a knowledge graph with embeddings and LLM-driven tools for predictive modeling and question-answering.

The paper benchmarks generative models for synthetic bulk RNA-seq data, finding trade-offs between utility, biological preservation, and privacy risks across different methods.

This paper introduces BioContextAI, an open-source initiative that provides standardized access to validated biomedical knowledge to improve the reliability of AI systems in biomedical research.

The paper constructs a knowledge graph and uses topological data analysis to uncover the gene regulatory network of tomato's response to multiple pathogens, identifying key transcription factors and regulatory hubs.

The paper integrates single-cell RNA-seq datasets across the GI tract to identify epithelial metaplasia in inflammatory diseases, showing its role in inflammation through immune cell interactions.

MetalinksDB provides a comprehensive database of metabolite-protein interactions, enabling context-specific analysis of metabolite-mediated cell-cell communication.

LIANA+ is a framework for analyzing cell-cell communication in single-cell and spatial transcriptomics data, integrating multiple methods and supporting multi-omics data.

The paper develops an integrative transcriptomic approach to uncover sexually dimorphic microRNA regulation in schizophrenia and bipolar disorder, identifying disease-related pathways and potential biomarkers.