MU OmicsLab focuses on computational toxicology and bioinformatics, with an emphasis on methods that can be applied across projects and shared with collaborators.

1) Bioinformatics for toxicology

  • RNA-seq / transcriptomics workflows (QC → DE → enrichment → signatures)
  • Context-aware interpretation: pathways, mechanisms, drug–gene relationships
  • Integrative thinking across experimental systems and public resources

2) Computational toxicology & mechanism modeling

  • Hypothesis-driven interpretation of toxicity phenotypes
  • Translating omics signals into pharmacodynamic targets and mechanism narratives
  • Risk-aware interpretation and reporting (limitations, uncertainty, reproducibility)

3) Image analysis & ML-assisted microscopy

  • Quantitative microscopy pipelines (segmentation, feature extraction, scoring)
  • ML for robust, scalable analysis of phase-contrast and fluorescence images
  • Reproducible reporting for experimental toxicology studies

4) RNA therapeutics & delivery (bioinformatics/logic support)

  • Design logic for RNA constructs aligned to cellular markers and pathways
  • Computational evaluation strategies for target selection and safety reasoning
  • Collaboration with experimental teams working on nanoparticles and delivery systems

5) AI-assisted research tools (research prototypes)

  • LLM-supported summarization and contextualization for complex biomedical knowledge
  • Transparent, safety-aware workflows for research—not clinical decision-making

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