Technology
DeepDependency
DeepDependency, which together with DeepNeo forms the DeepNeoVx® vaccine platform, is an algorithm that helps design highly effective vaccines that cancer cells cannot easily evade by analyzing the essentiality of genes from which neoantigens arise. The DeepDependency algorithm operates on a large-scale single-cell data warehouse. Neogenlogic’s data warehouse was built by systematically curating extensive single-cell data across diverse cancer types, collected through automated web crawling and refined through rigorous manual review.
- Neogenlogic’s proprietary algorithm, DeepDependency, identifies neoantigens originating from cancer dependencies—genes that are essential for the survival and fitness of tumor cells
- Cancer dependencies can be evaluated either through an AI-based method—the “dependency predictor”—or through a database-driven approach that utilizes Neogenlogic’s internal data warehouse.
Dependency predictor
- The dependency predictor is a deep learning–based method that identifies tumor-specific vulnerabilities by leveraging extensive CRISPR/RNAi screening data.
- Based on the gene regulatory network, it infers sample-specific dependency profiles directly from the patient’s transcriptome data.
Data warehouse
- Genes that are essential for cancer growth tend to be homogenously expressed across individual cells.
- Neogenlogic’s world-class single-cell data warehouse provides a framework for identifying cancer dependencies, enabling the development of more effective anti-cancer vaccines.
