PARCPolysubstance Addiction Research Center

Center core

Computational & Analytical Core

The Computational & Analytical Core provides statistical genetic, AI, data integration, bioinformatic, and network analysis support for the entire center. Its work converts the center’s standardized data streams into interpretable, mechanistic, and actionable outputs, cross-modal factors, biological subtypes, and ranked therapeutic targets, explicitly tied to behavior, imaging, and treatment effects.

Specific aims

  1. Polygenic predictions for every biobank sample. Genetic predictions for hundreds of behavioral traits will be computed for all individuals represented in the center’s biobanks, enabling novel experimental designs (such as selecting samples by predicted vulnerability). Predictions are updated quarterly as new samples and better models become available.
  2. Centralized bioinformatics through CCBB. The core integrates UC San Diego’s Center for Computational Biology & Bioinformatics (CCBB) into the center, providing bulk and single-cell RNA-seq analysis, statistical analysis, multi-omics integration, network-based modeling, consultation on experimental design, data visualization, workflow development, and training.
  3. Data harmonization and AI/ML readiness. Scalable data integration pipelines and biologically meaningful ontologies make the center’s datasets interoperable and AI/ML-ready, converting each modality into comparable sample-level representations, building the integration layer that links gut, vagus, and brain measurements to the biobank, and applying AI/ML and network biology to generate mechanistic, translatable insights.

Example network-based analysis: a gene interaction network colored by nervous-system expression, a scatter plot separating common-variant, rare-variant, and network-proximal genes by z-score, and a bar chart showing significantly more network-colocalized genes than expected by chance. Network biology in action: colocalization analysis identifies the genes bridging common and rare genetic variation, the style of integrative analysis the core applies across the center’s multi-omic datasets. (Figure from the Computational & Analytical Core research strategy.)

Access this core

Polygenic predictions are available for every sample in the Addiction Biobank, and analytical support is included with pilot awards. For bioinformatics collaborations, contact the center.

Core leadership

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