The article "Informing immunotherapy with multi-omics driven machine learning" by Yawei Li, Xin Wu, Deyu Fang, and Yuan Luo reviews the application of machine learning (ML) in predicting immunotherapy responses and identifying biomarkers associated with treatment outcomes. The authors highlight the importance of ML in leveraging multi-omic datasets to enhance understanding of the tumor microenvironment (TME) and improve immunotherapy effectiveness. Key topics include: 1. **Immunotherapy Response Prediction**: ML models, such as convolutional neural networks (CNNs) and support vector machines (SVMs), are used to predict immunotherapy responses based on genomic biomarkers and clinical features. These models can capture complex interactions among various molecular biomarkers, providing more nuanced insights into the TME. 2. **Biomarker Identification**: ML techniques, including LASSO regression, random forest (RF), and support vector machines (SVM), are employed to identify biomarkers associated with immunotherapy responses. These biomarkers can help in predicting patient outcomes and optimizing treatment strategies. 3. **TME Analysis**: ML models are used to analyze the TME, identifying characteristics and subcohorts with distinct responses to immunotherapy. This includes analyzing features such as tumor-infiltrating lymphocytes (TILs), cancer stem-like cells (CSCs), and cancer-associated fibroblasts (CAFs). 4. **Neoantigen Prediction**: ML algorithms, particularly deep learning (DL) models, are used to predict neoantigens, which are novel peptides formed by somatic mutations. These neoantigens can be recognized by immune cells, triggering immune responses against tumor cells. Models like NetMHC and MHCRoBERTa are used to predict binding affinity and immunogenicity of neoantigens. 5. **Challenges and Future Directions**: The article discusses challenges such as insufficient data, multi-omics data integration, and the need for more accurate neoantigen predictions. It also outlines future directions, including the development of models that can handle incremental datasets and real-time adaptation, and the importance of clinical validation to ensure the reliability of ML models in immunotherapy. Overall, the review emphasizes the potential of ML in advancing immunotherapy by providing precise biomarkers and enhancing the understanding of the complex interactions within the TME.The article "Informing immunotherapy with multi-omics driven machine learning" by Yawei Li, Xin Wu, Deyu Fang, and Yuan Luo reviews the application of machine learning (ML) in predicting immunotherapy responses and identifying biomarkers associated with treatment outcomes. The authors highlight the importance of ML in leveraging multi-omic datasets to enhance understanding of the tumor microenvironment (TME) and improve immunotherapy effectiveness. Key topics include: 1. **Immunotherapy Response Prediction**: ML models, such as convolutional neural networks (CNNs) and support vector machines (SVMs), are used to predict immunotherapy responses based on genomic biomarkers and clinical features. These models can capture complex interactions among various molecular biomarkers, providing more nuanced insights into the TME. 2. **Biomarker Identification**: ML techniques, including LASSO regression, random forest (RF), and support vector machines (SVM), are employed to identify biomarkers associated with immunotherapy responses. These biomarkers can help in predicting patient outcomes and optimizing treatment strategies. 3. **TME Analysis**: ML models are used to analyze the TME, identifying characteristics and subcohorts with distinct responses to immunotherapy. This includes analyzing features such as tumor-infiltrating lymphocytes (TILs), cancer stem-like cells (CSCs), and cancer-associated fibroblasts (CAFs). 4. **Neoantigen Prediction**: ML algorithms, particularly deep learning (DL) models, are used to predict neoantigens, which are novel peptides formed by somatic mutations. These neoantigens can be recognized by immune cells, triggering immune responses against tumor cells. Models like NetMHC and MHCRoBERTa are used to predict binding affinity and immunogenicity of neoantigens. 5. **Challenges and Future Directions**: The article discusses challenges such as insufficient data, multi-omics data integration, and the need for more accurate neoantigen predictions. It also outlines future directions, including the development of models that can handle incremental datasets and real-time adaptation, and the importance of clinical validation to ensure the reliability of ML models in immunotherapy. Overall, the review emphasizes the potential of ML in advancing immunotherapy by providing precise biomarkers and enhancing the understanding of the complex interactions within the TME.