About this Research Topic
In the field of cancer research, tumor immunotherapy represents a significant breakthrough with tremendous potential to transform cancer treatment protocols. However, the identification of effective immunological targets continues to pose formidable challenges. Traditional methods for immunotherapy target identification laboriously rely on clinical experience and laboratory experiments. These methods are time-consuming and often lack specificity, particularly within the heterogeneous nature of tumor environments. The variation in immune responses among patients, influenced by genetics, environmental factors, and tumor-specific characteristics, adds another layer of complexity to the search for viable targets.
This research topic aims to leverage the capabilities of artificial intelligence (AI) and machine learning (ML) to improve the identification process of immunotherapy targets in oncology. AI and ML are well-suited for processing vast amounts of biological data and recognizing patterns crucial for pinpointing potential targets. The integration of these technologies allows for the complex analysis of genomic, clinical, and immunomic datasets, facilitating the identification of new targets and the prediction of patient-specific immune responses. By harnessing deep learning, researchers can refine tumor antigen identification and tailor personalized therapeutic strategies, enhancing both the precision and effectiveness of immunotherapy.
To further advance the application of AI and ML in this critical area of cancer treatment, the scope of this research topic includes:
o Examination of machine learning methods for immune target screening.
o Utilizing AI to analyze the tumor microenvironment.
o Case studies on integrating multi-omics data for new immune target discovery.
o Deep learning applications in antigen prediction.
o Investigating the correlations between immune responses and tumor characteristics.
o AI-enhanced design of clinical trials and target validation.
o Exploring the potential and challenges of personalized immunotherapy through machine learning.
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.
This research topic aims to leverage the capabilities of artificial intelligence (AI) and machine learning (ML) to improve the identification process of immunotherapy targets in oncology. AI and ML are well-suited for processing vast amounts of biological data and recognizing patterns crucial for pinpointing potential targets. The integration of these technologies allows for the complex analysis of genomic, clinical, and immunomic datasets, facilitating the identification of new targets and the prediction of patient-specific immune responses. By harnessing deep learning, researchers can refine tumor antigen identification and tailor personalized therapeutic strategies, enhancing both the precision and effectiveness of immunotherapy.
To further advance the application of AI and ML in this critical area of cancer treatment, the scope of this research topic includes:
o Examination of machine learning methods for immune target screening.
o Utilizing AI to analyze the tumor microenvironment.
o Case studies on integrating multi-omics data for new immune target discovery.
o Deep learning applications in antigen prediction.
o Investigating the correlations between immune responses and tumor characteristics.
o AI-enhanced design of clinical trials and target validation.
o Exploring the potential and challenges of personalized immunotherapy through machine learning.
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.
