Cancer research has undergone a transformative shift with the advent of high-throughput technologies, resulting in expansive multi-omics datasets that encompass genomics, transcriptomics, epigenomics, proteomics, metabolomics, and the more recent fields of single-cell and spatial omics. These datasets offer a deeper understanding of tumor heterogeneity, the complexity of cellular mechanisms, and the intricacies of regulatory networks, while simultaneously identifying potential therapeutic targets. Despite the wealth of data, the inherent multidimensionality and heterogeneity pose significant challenges for conventional analytical methodologies, making advancements in analysis crucial.
Artificial intelligence (AI), including machine learning, deep learning, and explainable AI, offers sophisticated tools to tackle the challenges of multi-omics data integration and analysis. These tools are integral for efficient pattern recognition and mechanistic discovery, allowing for the creation of high-performance models for cancer diagnosis, prognosis, and treatment-response predictions. AI-driven approaches bridge computational analytics with biological validation, enriching precision oncology by enhancing our understanding of underlying mechanisms.
This Research Topic aims to transcend traditional predictive modeling by fostering AI-driven multi-omics research focused on mechanistic understanding and clinical application. The focus includes developing innovative AI algorithms to integrate and analyze complex, multi-layered omics data, elucidating critical molecular networks and cellular subtypes involved in tumor initiation, progression, and drug resistance. Strengthening clinical translation is imperative, with emphasis on validating pivotal findings through independent cohorts or experimental systems. The ultimate objective is to bolster the body of scientific evidence that underpins robust cancer diagnostics, risk assessments, and personalized therapeutic strategies.
With boundaries aimed at integrating AI in multi-omics for mechanistic and translational insight into cancer, we welcome articles addressing, but not limited to, the following themes:
• Integration and analysis of multi-layered omics data.
• Elucidation of molecular networks and cellular subpopulations in cancer.
• Development of explainable AI algorithms.
• Validation of discoveries in independent cohorts or experimental systems.
• AI-driven cancer diagnostics and therapeutic strategies.
This Research Topic encompasses Original Research, Method/Technology papers, Reviews, Mini-Reviews, and Perspectives that utilize AI to process multi-omics data, driving advances in understanding cancer mechanisms and precision oncology. Purely descriptive studies or those limited to public data without mechanistic interpretation are outside the scope of this collection.
Cancer research has undergone a transformative shift with the advent of high-throughput technologies, resulting in expansive multi-omics datasets that encompass genomics, transcriptomics, epigenomics, proteomics, metabolomics, and the more recent fields of single-cell and spatial omics. These datasets offer a deeper understanding of tumor heterogeneity, the complexity of cellular mechanisms, and the intricacies of regulatory networks, while simultaneously identifying potential therapeutic targets. Despite the wealth of data, the inherent multidimensionality and heterogeneity pose significant challenges for conventional analytical methodologies, making advancements in analysis crucial.
Artificial intelligence (AI), including machine learning, deep learning, and explainable AI, offers sophisticated tools to tackle the challenges of multi-omics data integration and analysis. These tools are integral for efficient pattern recognition and mechanistic discovery, allowing for the creation of high-performance models for cancer diagnosis, prognosis, and treatment-response predictions. AI-driven approaches bridge computational analytics with biological validation, enriching precision oncology by enhancing our understanding of underlying mechanisms.
This Research Topic aims to transcend traditional predictive modeling by fostering AI-driven multi-omics research focused on mechanistic understanding and clinical application. The focus includes developing innovative AI algorithms to integrate and analyze complex, multi-layered omics data, elucidating critical molecular networks and cellular subtypes involved in tumor initiation, progression, and drug resistance. Strengthening clinical translation is imperative, with emphasis on validating pivotal findings through independent cohorts or experimental systems. The ultimate objective is to bolster the body of scientific evidence that underpins robust cancer diagnostics, risk assessments, and personalized therapeutic strategies.
With boundaries aimed at integrating AI in multi-omics for mechanistic and translational insight into cancer, we welcome articles addressing, but not limited to, the following themes:
• Integration and analysis of multi-layered omics data.
• Elucidation of molecular networks and cellular subpopulations in cancer.
• Development of explainable AI algorithms.
• Validation of discoveries in independent cohorts or experimental systems.
• AI-driven cancer diagnostics and therapeutic strategies.
This Research Topic encompasses Original Research, Method/Technology papers, Reviews, Mini-Reviews, and Perspectives that utilize AI to process multi-omics data, driving advances in understanding cancer mechanisms and precision oncology. Purely descriptive studies or those limited to public data without mechanistic interpretation are outside the scope of this collection.