Technology
DeepDependency
DeepNeo와 함께 DeepNeoVx® 백신 플랫폼을 구성하는 DeepDependency는 신생항원이 만들어지는 유전자의 필수성 분석을 통해 암 세포가 피해갈 수 없는 높은 효율의 백신을 설계하는데 도움을 주는 알고리즘입니다. DeepDependency 알고리즘은 대규모 단일세포(single-cell) 데이터 웨어하우스를 기반으로 작동합니다. 네오젠로직(Neogenlogic)의 데이터 웨어하우스는 자동 웹 크롤링으로 수집한 다양한 암종에 대한 대규모 단일세포 데이터를 체계적인 수동 큐레이션으로 정제함으로써 구축되었습니다.
- 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.
