Integrating Multiomics Data with Deep Learning for Disease Risk Prediction
An engineering deep-dive into resolving high-dimensionality and cross-omic interactions using Multimodal VAEs, Graph Neural Networks, and Cross-Attention Transformers.
An engineering deep-dive into resolving high-dimensionality and cross-omic interactions using Multimodal VAEs, Graph Neural Networks, and Cross-Attention Transformers.
Step-by-step architectural breakdown for migrating standalone EC2 model deployments to fully orchestrated, scalable EKS clusters.
An engineering deep-dive into resolving dependency conflicts and compute scaling bottlenecks in biomedical data workflows using Nextflow and containerization.
How Graph Attention Networks work, why they matter for biological structure, and how to prototype them with PyTorch Geometric.
How to use Terraform to provision basic AWS storage and access controls for ML workloads without turning simple infrastructure into manual drift.
How user privileges, filesystem mounts, and scheduler constraints make Docker a poor default in HPC and why Apptainer remains the better execution model.
How to get useful experiment tracking with MLflow, cloud object storage, and minimal infrastructure instead of an oversized MLOps stack.
How to structure channels, processes, and configuration so your first Nextflow pipeline is production-ready instead of tutorial-only.
How to build lean Docker images for ML workloads without carrying notebook-era assumptions into production clusters.
How to design a cluster-friendly scRNA-seq workflow for QC, normalization, dimensionality reduction, and clustering without exhausting memory.