Master
English

Predictive Modeling of Immunotherapy Response Using Spatial Omics Profiling and Machine Learning

ContributorsCelestini, Dan
Number of pages72
Master program titleMaster in Bioinformatics and Data Analysis In Biology (BIADB)
Handover date2024-08-13
Abstract

This study explores predictive modeling of immunotherapy response in melanoma by integrating spatial omics profiling with machine learning. The research focuses on identifying spatial biomarkers that predict responses to immune checkpoint inhibitors. Utilizing imaging mass cytometry data, the study refines cell typing techniques and examines the spatial heterogeneity of tumor cell populations. Key findings include the identification of specific cell phenotypes and immune cell infiltration patterns that correlate with treatment outcomes. A random forest model was developed to predict immunotherapy response, highlighting a promising approach for enhancing precision oncology and personalizing treatment strategies.

Keywords
  • Melanoma
  • Oncology
  • Biomarkers
  • Plasma Cells
  • Spatial-omics
Citation (ISO format)
CELESTINI, Dan. Predictive Modeling of Immunotherapy Response Using Spatial Omics Profiling and Machine Learning. Master, 2024.
Main files (1)
Master thesis
accessLevelRestricted
Identifiers
  • PID : unige:179743
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Technical informations

Creation15/08/2024 08:49:08
First validation09/09/2024 15:18:55
Update time09/09/2024 15:18:55
Status update09/09/2024 15:18:55
Last indexation01/11/2024 11:57:20
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