Rapid progress in high-throughput omics data moves us one step closer to the datacalypse in life sciences. Biological entities are involved in intricate and complex interactions, in which uncovering the biological information from the biomolecule variation is of great significance. Despite the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex diseases remains limited. Increasing evidence shows that the effects of biomolecule variation are essential for a better understanding of complex diseases and can be reflected in medical imaging such as CT and MRI. We expect the research papers in this Research Topic to promote research on complex disease analysis, facilitating the discovery of new disease markers or exploring the relationship between biomolecule variation and medical imaging changes.
To encourage and improve the research on precision medicine as well as their applications in clinical treatment, we presented this special issue. Research related to analyzing complex diseases, exploring the relationship between biomolecule variation and medical imaging changes, and precision medicine linking to the patients’ biomolecule variations are welcome. The overall biomolecule variation of complex diseases will be revealed through the analysis of high-throughput omics data and the search for the internal relationship of their molecular variables. The databases, methods, and systematic analysis of patients’ genomic data could bring new opportunities for deeply understanding human complex diseases and developing precision medicine.
The recent advances in high-throughput technologies provide us an opportunity to study diseases and further contribute to precision medicine. These multi-omics big-data (such as genomic, transcriptomic, epigenomic, proteomic, medical imaging, etc.) are important sources to dissect the pathology of diseases (such as malignant tumors, endocrine system diseases, cardiovascular and cerebrovascular diseases) which facilitate the development of novel treatment targets and clinical strategies. Application of genome and functional omics data with genetic and phenotypic information can lead to discovering causative genes and pathways responsible for clinical phenotypes. The specific goal of this topic is to highlight the importance of high-throughput omics data and its potential usage in understanding the pathology of complex diseases. We aim to provide an overview of the current state-of-the-art high-throughput omics data learning methods, as well as their applications in clinical treatment. Potential topics include but are not limited to the following:
• Integration of high-throughput omics data to evaluate gene variation and association with complex diseases.
• Characterizing the causative genomic variations (such as somatic mutations, copy number variations, and SNPs) disturbing functions of complex diseases.
• Comprehensive analysis of biomolecular networks within the development of complex diseases.
• Biomedical image-based processing approaches and application of clinical diagnostic and therapeutic study.
• Experimental detection and validation of biomolecular variation contributing to personalized disease phenotype and treatment.
• Novel approaches to identify causative gene regulation events and highlight personalized treatments.
• Novel databases and tools to integrate high-throughput omics data and investigate the nature of complex diseases and their response to therapy.
Rapid progress in high-throughput omics data moves us one step closer to the datacalypse in life sciences. Biological entities are involved in intricate and complex interactions, in which uncovering the biological information from the biomolecule variation is of great significance. Despite the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex diseases remains limited. Increasing evidence shows that the effects of biomolecule variation are essential for a better understanding of complex diseases and can be reflected in medical imaging such as CT and MRI. We expect the research papers in this Research Topic to promote research on complex disease analysis, facilitating the discovery of new disease markers or exploring the relationship between biomolecule variation and medical imaging changes.
To encourage and improve the research on precision medicine as well as their applications in clinical treatment, we presented this special issue. Research related to analyzing complex diseases, exploring the relationship between biomolecule variation and medical imaging changes, and precision medicine linking to the patients’ biomolecule variations are welcome. The overall biomolecule variation of complex diseases will be revealed through the analysis of high-throughput omics data and the search for the internal relationship of their molecular variables. The databases, methods, and systematic analysis of patients’ genomic data could bring new opportunities for deeply understanding human complex diseases and developing precision medicine.
The recent advances in high-throughput technologies provide us an opportunity to study diseases and further contribute to precision medicine. These multi-omics big-data (such as genomic, transcriptomic, epigenomic, proteomic, medical imaging, etc.) are important sources to dissect the pathology of diseases (such as malignant tumors, endocrine system diseases, cardiovascular and cerebrovascular diseases) which facilitate the development of novel treatment targets and clinical strategies. Application of genome and functional omics data with genetic and phenotypic information can lead to discovering causative genes and pathways responsible for clinical phenotypes. The specific goal of this topic is to highlight the importance of high-throughput omics data and its potential usage in understanding the pathology of complex diseases. We aim to provide an overview of the current state-of-the-art high-throughput omics data learning methods, as well as their applications in clinical treatment. Potential topics include but are not limited to the following:
• Integration of high-throughput omics data to evaluate gene variation and association with complex diseases.
• Characterizing the causative genomic variations (such as somatic mutations, copy number variations, and SNPs) disturbing functions of complex diseases.
• Comprehensive analysis of biomolecular networks within the development of complex diseases.
• Biomedical image-based processing approaches and application of clinical diagnostic and therapeutic study.
• Experimental detection and validation of biomolecular variation contributing to personalized disease phenotype and treatment.
• Novel approaches to identify causative gene regulation events and highlight personalized treatments.
• Novel databases and tools to integrate high-throughput omics data and investigate the nature of complex diseases and their response to therapy.