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

Diplôme Master Predictive Modeling of Immunotherapy Response Using Spatial Omics Profiling and Machine Learning eng 2024-08-13 author Celestini Dan 990264 director Michielin Olivier 903944 Département de médecine (DEMED) director Cuendet Michel 418547 director Cheng Phil 0000-0003-2940-006X 418075 director Sanchez-Mazas Alicia 22545 Département de génétique et évolution (Genev) (BIANI) 72 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. Melanoma Oncology Biomarkers Plasma Cells Spatial-omics Dewey 616 Dewey 570 Master in Bioinformatics and Data Analysis In Biology (BIADB) 30 Section de biologie 16 56 Département de médecine 221 false 1432 Découverte de biomarqueurs numériques pour l'oncologie de précision 1059 dd94d628-3d3a-42cd-88c6-abd2c2eff80a Master thesis Restricted Biology_Master_Thesis_Dan_Celestini.pdf 38573829 application/pdf 5dbf5bfbceb98669313d7dcc469033a1f7d5167ecee6b15a0fef56774ebb5f53 Vignette Public Title_new_final.png 437302 image/png openAccess Faculté des sciences / Section de biologie Faculté de médecine / Section de médecine clinique / Département de médecine

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Predictive Modeling of Immunotherapy Response Using Spatial Omics Profiling and Machine Learning				2024	36	1

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