About this Research Topic
Immune-mediated disorders occur when the body's immune system mistakenly attacks healthy cells and tissues, such as autoimmune diseases and infections, or unable to detect unnormal cellular progress, such as cancer. The immune system is highly complex and involves numerous types of cells, proteins, and signaling pathways. Understanding how these components interact and contribute to disease is a significant challenge. Moreover, the limited number of data hinders the in-depth investigation of immune-mediated disorders.
Advancements in omics technologies, coupled with decreasing data generation costs, have enabled detailed profiling of disease molecular features across various levels, including bulk tissues, single cells, and spatially resolved regions. The growing volume of omics data enhances the capacity to inform preclinical studies, offering insights into disease mechanisms, identification of molecular therapeutic targets, and discovery of biomarkers predictive of treatment response. Identifying robust, predictive, and interpretable biomarkers is a cornerstone for advancing personalized medicine and targeted therapies. Additionally, multi-omics data play a crucial role in identifying biomarkers for diagnostic and prognostic purposes.
Integrating multi-omics datasets and cross-platform data is critical for expanding the feature and sample space required for big data analysis, often leveraging deep learning methods. Over the past two decades, numerous data integration techniques have been developed to address batch effects across datasets and platforms. The choice of integration method significantly impacts the reliability and reproducibility of identified biomarkers or molecular signatures. Therefore, developing advanced integration methods or normalization-free algorithms holds great potential to refine biomarker discovery processes and improve disease diagnostics.
We welcome the submission of Original Research papers, Methods papers, as well as Review articles on algorithms and applications of omics data analysis especially in the context of biomarker identification and omics data integration. Please note that studies relating to the prediction of clinical outcomes require some validation of findings. Topics of interest include but are not limited to:
• Multi-Omics Data on Clinical Applications: Integrative approaches combining multi-scale clinical data and multi-omics data for the screening, diagnosis, prognosis, treatment, and monitoring of immune-related diseases.
• Algorithm Development: Novel machine learning and deep learning techniques designed for predicting clinical outcomes in immune diseases, with a focus on model interpretability, transparency, and clinical applicability.
• Biomarker Discovery: Identification, validation, and characterization of stable biomarkers across multi-omics data types, including those with diagnostic, prognostic, and therapeutic potential.
• Data Integration and Normalization Techniques: Development and validation of methodologies for multi-scale and multi-platform omics data integration, including advanced batch effect correction, normalization-free and high-dimensional data processing approaches to enhance biomarker reproducibility and reliability.
• Translational Applications: Case studies, clinical trials, and real-world applications where integrated omics data inform patient stratification, predict treatment outcomes, or support tailored therapeutic approaches.
• Toolkits and Databases for Immunomics Analysis: Development and application of open-source tools, pipelines, platforms, and omics-data specifically designed for large-scale immunology.
Advancements in omics technologies, coupled with decreasing data generation costs, have enabled detailed profiling of disease molecular features across various levels, including bulk tissues, single cells, and spatially resolved regions. The growing volume of omics data enhances the capacity to inform preclinical studies, offering insights into disease mechanisms, identification of molecular therapeutic targets, and discovery of biomarkers predictive of treatment response. Identifying robust, predictive, and interpretable biomarkers is a cornerstone for advancing personalized medicine and targeted therapies. Additionally, multi-omics data play a crucial role in identifying biomarkers for diagnostic and prognostic purposes.
Integrating multi-omics datasets and cross-platform data is critical for expanding the feature and sample space required for big data analysis, often leveraging deep learning methods. Over the past two decades, numerous data integration techniques have been developed to address batch effects across datasets and platforms. The choice of integration method significantly impacts the reliability and reproducibility of identified biomarkers or molecular signatures. Therefore, developing advanced integration methods or normalization-free algorithms holds great potential to refine biomarker discovery processes and improve disease diagnostics.
We welcome the submission of Original Research papers, Methods papers, as well as Review articles on algorithms and applications of omics data analysis especially in the context of biomarker identification and omics data integration. Please note that studies relating to the prediction of clinical outcomes require some validation of findings. Topics of interest include but are not limited to:
• Multi-Omics Data on Clinical Applications: Integrative approaches combining multi-scale clinical data and multi-omics data for the screening, diagnosis, prognosis, treatment, and monitoring of immune-related diseases.
• Algorithm Development: Novel machine learning and deep learning techniques designed for predicting clinical outcomes in immune diseases, with a focus on model interpretability, transparency, and clinical applicability.
• Biomarker Discovery: Identification, validation, and characterization of stable biomarkers across multi-omics data types, including those with diagnostic, prognostic, and therapeutic potential.
• Data Integration and Normalization Techniques: Development and validation of methodologies for multi-scale and multi-platform omics data integration, including advanced batch effect correction, normalization-free and high-dimensional data processing approaches to enhance biomarker reproducibility and reliability.
• Translational Applications: Case studies, clinical trials, and real-world applications where integrated omics data inform patient stratification, predict treatment outcomes, or support tailored therapeutic approaches.
• Toolkits and Databases for Immunomics Analysis: Development and application of open-source tools, pipelines, platforms, and omics-data specifically designed for large-scale immunology.
Keywords: Immune-mediated disorders, Biomarker discovery, Multi-omics integration, Data normalization techniques, Precision medicine
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.
