How Ardigen’s daGama framework helped a drug discovery company identify crucial genetic targets for cancer treatment?
Identifying essential genes in cancer is a cornerstone of targeted drug discovery. Yet, the integration of diverse multi-omics datasets and the complexity of advanced machine learning methods pose significant challenges. In this case study, we explore how a growing drug discovery company partnered with Ardigen to explore the use of AI and multi-omics data integration for breakthrough insights into gene essentiality.
Challenge
The client faced three key hurdles:
- Lack of in-house Machine Learning expertise: Despite having rich omics datasets from patients and cell lines, the client lacked the ML capabilities to extract actionable insights.
- Data heterogeneity: Combining in vivo and in vitro multi-omics profiles introduced batch effects and inconsistencies.
- Need for rapid, reliable outcomes: Time-sensitive research required fast, yet scientifically robust, results.
Approach
To tackle these challenges, Ardigen deployed its proprietary daGama Framework, blending computational biology expertise with cutting-edge machine learning methods. Key elements of the approach included:
- Variational Autoencoders (VAE): Used for learning compact, informative representations of genetic profiles.
- Multimodal VAE (MVAE): Integrated various omics data types into a unified, consistent format.
- Batch effect correction and domain adaptation: Ensured data harmonization across sources, improving cross-domain reliability.
This robust strategy enabled the creation of a harmonized, multi-dimensional view of gene activity across both patient-derived and cell line datasets.
Results
- >70% improvement in predictive performance with domain adaptation, highlighting the importance of advanced correction techniques.
- 10+ synthetically lethal gene pairs identified, including novel discoveries and known pairs, validating the method’s accuracy.
- Successfully identified essential genes across multiple cancer types, delivering a critical foundation for targeted therapy research.
Through collaboration with Ardigen and the application of its daGama Framework, the client transformed a fragmented data challenge into a high-impact research success. This project not only enhanced the state of the art in gene essentiality prediction but also accelerated the client’s drug discovery efforts with scientifically validated insights.
