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Artificial intelligence in pharma and life sciences
Focus on multi-omics analysis and explainable, interpretable AI to drive the digital transformation of the biopharma sector, with the goal of accelerating drug discovery, novel curative therapies, diagnostics, and personalized healthcare.
Roadmap of artificial intelligence for life sciences
The pharmaceutical industry is rapidly catching up with major companies investing heavily in AI technologies. This shift presents unprecedented opportunities to revolutionize drug development and patient care by making use of those AI tools.
Key components of the development of AI tools:
- Multi-modal data analysis
- Explainable & interpretable AI
- Scalable & robust data pipelines
Multi-modal data analysis
We integrate omics, imaging, and temporal data with machine learning to analyze diverse data types, enabling precision medicine through tailored biomarker discovery and predictive therapeutics. Our MultiOmix framework is designed to combine various types of data (genomics, transcriptomics, proteomics) for enhanced and personalized medicine applications.
MultiOmix represents a breakthrough in multimodal data integration, specifically designed to combine various types of omics data (genomics, transcriptomics, proteomics).
Advanced cell analysis
CSEM's multi-modal approach to single-cell analysis and organoids leverages cutting-edge technologies to deliver comprehensive cellular insights through AI-powered clustering based on visual and molecular similarities of cell-organoid images.
Applications in precision medicine
Our multi-modal analysis capabilities advance precision medicine by integrating data in order to understand disease mechanisms, identify biomarkers for complex conditions, and predict treatment responses.
Explainable and interpretable AI
We combine interpretable representation learning, explainable knowledge distillation, and post-hoc interpretability methods to build biologically informed models and ensure model transparency. CSEM's approach to explainable AI in life sciences delivers three core benefits:
- enhanced trust through transparent AI model predictions
- support for regulatory compliance readiness
- scientific progress via improved biomarker identification.
This framework helps researchers and clinicians make better decisions and encourages teamwork between data scientists and experts. It combines methods such as biologically based learning and knowledge sharing from complex to simpler models. These methods are useful to discover biomarkers, develop drugs, personalize medicine, and support clinical decisions, ensuring AI is trustworthy and practical in real-world situations.
Scalable and robust data pipelines
CSEM's MultiOmix framework demonstrates our expertise in the efficient processing of complex biological data types. We develop modular, cloud-ready architectures that scale with data volumes. Our automated platforms use deep learning to improve accuracy, especially in microtissue analysis and drug testing. This end-to-end approach enables reliable data processing—from collection to analysis— driving innovation in drug development and personalized medicine.
Our modular framework allows researchers to:
- Rapidly prototype and adapt to novel research problems
- Combine existing algorithms and pre-trained models efficiently
- Process complex biological datasets with high accuracy.
Explore some of our developments in AI for life sciences
DeepBINN: Robust biomarker identification with biologically informed AI
Biologically informed neural networks (BINNs) enhance the interpretability of deep learning models, helping to identify disease-relevant biomarkers and pathways. By integrating prior knowledge of biomarker-pathway relationships into sparse networks, BINNs perform well in various tasks. However, the robustness of these biomarker signatures is underexplored. We are proud to introduce DeepBINN, a new architecture that learns pathway importance during training, enhancing interpretability without post-hoc methods. In a preliminary study, DeepBINN effectively stratified patients with septic acute kidney injury, showing improved interpretability and robustness while maintaining top performance.
For further reading on advancements in Brain-on-Chip technology and their applications in brain disease research, you can refer to this detailed IEEE article: Innovative Brain-on-Chip Technology for Neurological Research. (Please note that access to this article requires a subscription or purchase)
AI-based transfer of research results from animals to humans
An innovative deep learning model is being developed to bridge the translational gap between animal models and human applications in biomedical research. Traditionally, rodents are used to study disease mechanisms and test new drug candidates, but significant biological differences between rodents and humans often prevent direct application of these findings to human health. This AI-driven approach will enhance the transferability of research results from animals to humans, ensuring more accurate and applicable findings. The initial case study will focus on medulloblastoma, a rare and difficult-to-treat type of cerebellar cancer, marking a significant improvement in the efficacy of biomedical research and accelerating the development of novel therapies.
This project "AI-based transfer of research results from animals to humans" has been awarded a 2024 FreeNovation Grant by Novartis, recognizing its potential to transform translational medicine through the power of artificial intelligence.
Use cases for AI applications in Biotech
Explore some of our developments in AI for life sciences