Building open, reproducible frameworks for multi-omic data integration, spatial transcriptomics, and computational pathology.

My work focuses on developing interpretable and reproducible AI frameworks for cancer genomics, uniting biological prior knowledge with multi-omic and spatial data. The central goal is to replace black-box prediction with mechanistic understanding—models that not only perform well but explain how genomic alterations, perturbations, and drug responses reshape cellular states. Each framework in this series emphasizes pathway- and network-level interpretability, cross-dataset generalization, and transparent benchmarking, establishing reproducible standards for computational oncology. Through this approach, I aim to bridge machine learning, systems biology, and translational research, advancing models that predict, explain, and validate biological mechanisms.

The following key projects are part of the MM-KPNN framework family, a unified effort to develop concept-bottleneck AI models that embed biological knowledge directly into network architecture—ensuring interpretability, reproducibility, and mechanistic insight across multi-omic and spatial data.

A modular and interpretable graph framework for spatial transcriptomics in the tumor microenvironment.

• Combines Graph Attention Networks (GAT) with knowledge-primed decoding
• Explains immune exclusion, stromal remodeling, and therapy-induced rewiring
• Outputs attention maps, pathway overlays, and ligand–receptor driver rankings

Interpretable multimodal neural network integrating scRNA-seq + scATAC-seq using biological priors.

• Decoder constrained by pathway and TF nodes
• Provides mechanistic attributions at the pathway and regulator levels
• A reproducible framework for multimodal interpretability and benchmarking

Pathway-bottleneck graph neural network for drug-sensitivity prediction across pharmacogenomic panels.

• Integrates multi-omic features, drug descriptors, and prior knowledge graphs
• Focuses on cross-panel generalization (e.g., CCLE → GDSC)
• Provides pathway-level interpretability and reproducible benchmarking

Extends MM-KPNN to model drug and CRISPR perturbation responses at single-cell resolution.

• Implements pathway and TF bottlenecks for interpretability
• Measures attribution stability and supports counterfactual pathway editing
• Designed for robust, cross-dataset perturbation benchmarking

A modular framework for computational analysis of organoid systems.

• Addresses reproducibility, heterogeneity, fidelity, integration, and prediction
• Integrates RNA and protein modalities with interpretable ML
• Demonstrates end-to-end reproducibility through documented, result-embedded notebooks

Spatial mapping of tumor and metastatic architecture using 10x Visium transcriptomics.

• Integrates curated gene programs to define epithelial, immune, stromal, and proliferative regions
• Reveals spatial organization and regional heterogeneity across breast tumors and lymph node metastases
• Fully documented, end-to-end notebook with embedded results and biological interpretation

End-to-end pipeline for structural variant discovery and annotation using PacBio long-read sequencing.

• Implements clinical annotation (ACMG/AMP) and variant filtering
• Includes functional scoring and visualization modules
• Designed for scalable deployment in HPC environments

Modular framework for rare-variant burden analysis in genomic cohorts.

• Supports SKAT, SKAT-O, and extended statistical methods
• Implements functional weighting and population correction
• Provides reproducible variant filtering and QC workflows

Systems biology workflow for reconstructing gene-regulatory networks.

• Integrates TF–target priors and expression-based inference
• Performs network topology and modularity analysis
• Identifies functionally enriched regulatory modules

Gene co-expression analysis pipeline using WGCNA.

• Identifies expression modules and hub genes
• Evaluates biological function and module preservation
• Applies to bulk and single-cell RNA-seq datasets

Workflow for secure, efficient genomic data transfer using Globus.

• Integrates HPC environments and folder structuring
• Enables checksum validation and metadata tracking
• Ensures reproducible data sharing for collaborative projects

Sally Yepes 📧 sallyepes233@gmail.com
🔗 GitHub: Sally332