About this Research Topic
The development of multi-omics techniques including genomics, transcriptomics, proteomics, metabolomics, translatomics, epigenomics, metagenomics, immune repertoire sequencing, single-cell omics, and spatial transcriptomics, enables the understanding of the disease mechanism and the complexity of disease biology at an unprecedented scale. In the early stage, the majority of the studies focus on using the individual omics technologies to explore the insight into the pathogenesis of the complex diseases, and the accompanying analysis pipelines have been constantly improved and refined, despite the fact that further standardization across the omics platforms remains to be required. Recently, due to the reduced cost and the broad application of these technologies, we have been entering a new era of leveraging more than one type of omics techniques along with clinical phenotyping to look at multi-faceted aspects of the disease mechanisms and integrating the multi-omics data in a more informative way.
The breakthrough of the novel technologies offers the opportunities to generate a comprehensive and holistic map of molecular changes leading to the diseases at a systematic level. In addition, multiple modality studies will facilitate the validation of the findings through the analyses of different data generated from the same set of samples and provide more reliable results. In particular, the unique features of multi-omics datasets are high-dimensional and distinct measurement units, which requires the dimension reduction techniques and normalization across different types of data. Accordingly, the new approaches have been developed to cope with the demands of analyzing, processing and interpreting the multi-omics datasets, such as topological analysis, tensor decomposition, machine learning, deep learning, similarity network fusion, and data visualization.
This collection aims to bring together the research papers, reviews and expert opinions on the innovation of the multi-omics methods including experiments and data analyses and their applications in the studies of human diseases such as common diseases, rare diseases and various types of cancers, with a focus on the integration of multiple dimensional datasets. It will provide excellent examples and guidelines for how effectively these new techniques can aid in the current studies on human health and diseases.
We welcome submissions from the following areas with a focus on the study of diseases using at least two omics techniques:
• Multi-omics data analysis pipeline and methods development
• The application of the multi-omics techniques in the context of common diseases, rare diseases and cancers
• Construction of databases and resources housing the multi-omics datasets
• Benchmarking the approaches of multi-omics data analysis
• Current challenge and future perspective
• Integration of multi-omics data
• Identify the biomarkers for early diagnosis, prognosis prediction, disease severity classification, disease phenotypes, subtypes and endotypes, personalized treatment and therapeutic target development through experimental design and data analysis from multi-omics platforms
• Investigation of disease progression through longitudinal sample collection and time-series data analysis
The breakthrough of the novel technologies offers the opportunities to generate a comprehensive and holistic map of molecular changes leading to the diseases at a systematic level. In addition, multiple modality studies will facilitate the validation of the findings through the analyses of different data generated from the same set of samples and provide more reliable results. In particular, the unique features of multi-omics datasets are high-dimensional and distinct measurement units, which requires the dimension reduction techniques and normalization across different types of data. Accordingly, the new approaches have been developed to cope with the demands of analyzing, processing and interpreting the multi-omics datasets, such as topological analysis, tensor decomposition, machine learning, deep learning, similarity network fusion, and data visualization.
This collection aims to bring together the research papers, reviews and expert opinions on the innovation of the multi-omics methods including experiments and data analyses and their applications in the studies of human diseases such as common diseases, rare diseases and various types of cancers, with a focus on the integration of multiple dimensional datasets. It will provide excellent examples and guidelines for how effectively these new techniques can aid in the current studies on human health and diseases.
We welcome submissions from the following areas with a focus on the study of diseases using at least two omics techniques:
• Multi-omics data analysis pipeline and methods development
• The application of the multi-omics techniques in the context of common diseases, rare diseases and cancers
• Construction of databases and resources housing the multi-omics datasets
• Benchmarking the approaches of multi-omics data analysis
• Current challenge and future perspective
• Integration of multi-omics data
• Identify the biomarkers for early diagnosis, prognosis prediction, disease severity classification, disease phenotypes, subtypes and endotypes, personalized treatment and therapeutic target development through experimental design and data analysis from multi-omics platforms
• Investigation of disease progression through longitudinal sample collection and time-series data analysis
Keywords: Multi-omics, human disease, data analysis, disease progression, therapeutic drug design
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.
