Building a Machine Learning Model on Spatial Transcriptomic Data to Support Targeted Immuno-Oncology Drug Development
Time: 11:30 am
day: Conference Day 2
Details:
- Uncovering strategies to train a machine learning model on limited high-cost Xenium data to enhance drug development
- Applying this model to H&E data to annotate cell types and observe patterns, maximizing the utility of currently available datasets
- Investigating the spatial patterns in these annotated images to identify potential correlations between spatial distributions of cells and clinical prognosis, offering new insights into disease progression and outcomes