The rapid advancement of tumor immunotherapy has generated new hope for cancer treatment. However, identifying effective immunotherapy targets remains a significant challenge. Traditional methods often depend on experience and laboratory studies, which are not only time-consuming and labor-intensive, but may also lack specificity. In the highly heterogeneous tumor microenvironment, the diversity of immune targets and their dynamic changes complicate target screening. Furthermore, individual patients' immune responses vary considerably based on genetic, environmental, and tumor characteristics, further exacerbating the difficulty of identifying and validating new targets. Therefore, it is crucial to employ novel technical approaches to enhance the efficiency of target discovery.
In this context, the integration of artificial intelligence (AI) and machine learning (ML) offers new promise for identifying immune targets. AI and ML possess robust data processing and pattern recognition capabilities, enabling the extraction of valuable features from complex biological data. By analyzing large-scale genomic, clinical, and immunomic datasets, these technologies have begun to uncover potential immune targets. Additionally, through the application of deep learning models, researchers can predict immune responses, identify tumor antigens, and develop personalized immunotherapy regimens. An increasing number of studies have demonstrated that AI and ML not only enhance the accuracy and efficiency of target screening but also provide substantial support for clinical translation.
This research Topic aims to enhance the application of artificial intelligence and machine learning in the identification of tumor immunotherapy targets, thereby providing a more robust scientific foundation for the early diagnosis and treatment of diseases. We welcome submissions that focus on the role of artificial intelligence and machine learning in identifying tumour-related immunotherapy targets. The specific scope includes:
1) Research on immune target screening methods utilizing machine learning;
2) The application of AI in analyzing the tumor microenvironment;
3) Case studies that integrate multi-omics data to identify novel immune targets;
4) The use of deep learning in advancing antigen prediction;
5) Studies investigating the relationship between immune responses and tumor characteristics; 6) AI-driven design of clinical trials and verification of targets;
7) Applications and challenges of machine learning in personalized immunotherapy.
Please note that manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this Research Topic.
Keywords:
Artificial Intelligence, Machine Learning, immune cell subpopulations, multi-omics, antigen prediction, Immune-Related Therapeutic Targets
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
The rapid advancement of tumor immunotherapy has generated new hope for cancer treatment. However, identifying effective immunotherapy targets remains a significant challenge. Traditional methods often depend on experience and laboratory studies, which are not only time-consuming and labor-intensive, but may also lack specificity. In the highly heterogeneous tumor microenvironment, the diversity of immune targets and their dynamic changes complicate target screening. Furthermore, individual patients' immune responses vary considerably based on genetic, environmental, and tumor characteristics, further exacerbating the difficulty of identifying and validating new targets. Therefore, it is crucial to employ novel technical approaches to enhance the efficiency of target discovery.
In this context, the integration of artificial intelligence (AI) and machine learning (ML) offers new promise for identifying immune targets. AI and ML possess robust data processing and pattern recognition capabilities, enabling the extraction of valuable features from complex biological data. By analyzing large-scale genomic, clinical, and immunomic datasets, these technologies have begun to uncover potential immune targets. Additionally, through the application of deep learning models, researchers can predict immune responses, identify tumor antigens, and develop personalized immunotherapy regimens. An increasing number of studies have demonstrated that AI and ML not only enhance the accuracy and efficiency of target screening but also provide substantial support for clinical translation.
This research Topic aims to enhance the application of artificial intelligence and machine learning in the identification of tumor immunotherapy targets, thereby providing a more robust scientific foundation for the early diagnosis and treatment of diseases. We welcome submissions that focus on the role of artificial intelligence and machine learning in identifying tumour-related immunotherapy targets. The specific scope includes:
1) Research on immune target screening methods utilizing machine learning;
2) The application of AI in analyzing the tumor microenvironment;
3) Case studies that integrate multi-omics data to identify novel immune targets;
4) The use of deep learning in advancing antigen prediction;
5) Studies investigating the relationship between immune responses and tumor characteristics; 6) AI-driven design of clinical trials and verification of targets;
7) Applications and challenges of machine learning in personalized immunotherapy.
Please note that manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this Research Topic.
Keywords:
Artificial Intelligence, Machine Learning, immune cell subpopulations, multi-omics, antigen prediction, Immune-Related Therapeutic Targets
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.