USE CASE

AI Patient Stratification Workflow

Data types in this use case: miRNA sequencing, Digital Pathology, Clinical, PK

The Importance of Precise Patient Stratification in Precision Medicine

Patient stratification is a process in drug discovery and development that involves categorizing patients into subgroups based on various characteristics such as genetics, molecular profile, or clinical data. This stratification is fundamental for precision medicine, to achieve tailored medical treatments to individual patients or patient groups based on these characteristics.

Patient stratification allows drug developers to achieve the following:

  • Enhanced Drug Efficacy and Safety: By understanding which patient groups are more likely to respond to a certain drug, developers can create more effective and safer treatments. It reduces the ‘one-size-fits-all’ approach.
  • Efficient Clinical Trials: Stratification can lead to more efficient clinical trials by selecting patient populations more likely to benefit from the drug, thereby reducing trial size and cost, and avoids waste of time and resources.
  • Reduction in Drug Development Failure: Many drugs fail in late-stage trials due to lack of efficacy in a broad patient population. Stratification can identify responsive subpopulations early, reducing failure rates.
  • Precision Medicine: It facilitates the development of precision medicine, allowing treatments to be tailored to specific patient subgroups.

Achieving Precise Patient Stratification is Challenging

  • Complexity of Diseases: Many diseases, like cancer, have complex and heterogeneous pathologies, making it challenging to identify clear stratification markers.
  • Ethical and Regulatory Considerations: Stratification raises ethical concerns about  access to treatment and patient privacy, such as access to genetic information. 
  • Cost and Resource Intensity: The process of identifying and validating biomarkers for stratification is often expensive and resource-intensive.
  • Data Integration and Analysis: Integrating and analyzing vast amounts of multi-modal data (genomic, proteomic, clinical, etc.) to identify relevant stratification markers is technically challenging.

Current Patient Stratification Methods Have Numerous Limitations

How AI Can Help Stratify Patients for Clinical Trials

Sonrai’s advanced technology brings precise patient stratification to life. Through clustered scatter plots, heatmaps, and interactive tools, Sonrai empowers the exploration and filtering of patient data effortlessly. We use machine learning tools to identify distinct subgroups and clusters, providing the insights that give teams the confidence to move forward.

Image 1. Sonrai Discovery enables users to easily visualize, explore and filter data

Select Disease Status Filtering Criteria

Patient Stratification Filter

Image 2. Users can select disease status to help visualize data

Utilize ML Tools to Identify Subgroups and Clusters

Image 3. Sonrai Discovery allows users to see insights from limited sample sizes

With Sonrai Discovery, even small sample sizes can yield valuable insights. Users can select line charts or bar charts to display trends and insights extracted from small sample sizes over time or across different patient groups.

Explore Longitudinal Data

Patient Stratification Pic 5

Image 4. Users can select how they want to visualize their data

See Individual Patients Data

Patient Stratification Pic 6

Image 5. Data can also be displayed per individual patient, enabling researchers to see all available data

Find Individual Patients with Features of Interest

Image 6. Users can identify features of interest to find individual patients in datasets

Explore Outcomes Versus Different Features

Survival Plot Patient Stratification

Image 7. Survival analysis exploring patients’ prognostics based on immune cells PDL1 expression. 

Multi-Modal Data Integration

To highlight the integration and analysis of diverse data types, a network graph or a Sankey diagram showing how different types of data, such as multi-omics data, imaging, clinical records, and biomarker measurements, are interconnected.

Integrate Your Data Sources

Merge Your Multi-Modal Data

Easily Find Clusters, Batch Effects and Outliers

Merge Your Multi-Modal Data

Visualize and Analyze Multi-Modal Data

Instantly Explore Associated Imaging Data

Image 8. Sonrai Discovery allows you to integrate your multi-omic data with imaging and other types of data.

Explore Precise Patient Stratification

Make data-driven decisions and discover meaningful patterns. Explore our capabilities and transform the future of drug development.

Reproducibility and Interpretability

Scientists can easily use our no-code applications to create reproducible and robust machine-learning pipelines. Sonrai Discovery includes visualization techniques and feature importance plots, assisting scientists in creating explainable machine learning models for their biomarkers discovery projects. 

Bioinformaticians and data scientists can use our managed Python and R notebooks to create custom pipelines with outputs that can be easily shared with scientists and stakeholders using our collaborative platform.

Quickly Identify Clusters

Image 9. Users can use various visualization techniques to identify clusters in their data

Easily Configure ML Models with No-Code

Image 10. Sonrai Discovery enables code and no-code working environment to suit both bioinformaticians and non-coding researchers

Identify Key Biomarkers From Your Model

Image 11. Use ML tools such as XGBoost to identify key biomarkers for patient stratification. 

Validate Biomarkers Identified by Your Model

Image 12. Once identified, users can validate their biomarkers within the same platform

Patient stratification is pivotal in the development of precision medicine. Integrating data using AI and machine learning enables precise and efficient identification of relevant patient subgroups and accelerates the development of novel treatments. AI can help bring more effective drugs to more people, improving patient outcomes and reducing costs.

Case Study: Patient Stratification

See how we helped a leading US pharmaceutical company focused on precision medicine overcome several challenges in its quest to deliver personalized treatments to patients.

Discover how companies worldwide grow with Sonrai. Explore all our case studies.

Multi-Modal Biomarker Discovery

Discover how Sonrai's integration of genomic, proteomic, imaging, and clinical data accelerated multi-modal biomarker discovery.

What is tSNE and when should I use it?

T-distributed Stochastic Neighbourhood Embedding (tSNE) is an unsupervised Machine Learning algorithm developed in 2008 by Laurens van der Maaten and Geoffery Hinton.

AI Biomarker Discovery Workflow

Explore an end-to-end AI biomarker discovery workflow using Sonrai Discovery. Discover just how easy it can be.

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