In recent decades, the advancements in high-throughput genomics technology allowed for cost and time affordable large-scale research projects. As a matter of fact, several governments around the world have initiated biobank projects to investigate the genetic profiles of their citizen and to have a long-term follow-up of the health data at a population level. The major goal of such biobank projects is to elucidate the etiology of diseases and to establish the linkage between phenotypes and genetic profiles. Several studies have been conducted to achieve this goal. However, when analysing the massive data from these large-scale biobank projects, many challenges have arisen, including the curse of high dimensionality and the difficulty in incorporating the biological structure in the genetics data.
The first goal of this Research Topic is to provide a comprehensive overview of the current advances in scientific and clinical knowledge deriving from biobank data analysis. We welcome algorithms and statistical models performing an integrated analysis of multiple-omics data in health and disease, with the aim to establish an association between the genetic profiles and biological phenotypes. The second goal of this collection is to discuss the difficulties and challenges of biobank data analysis. For example, the genetics data, such as DNA sequence variant, DNA copy number and DNA methylation, have a hierarchy and thus the incorporation of the biological structure in the analysis protocols may be crucial.
We welcome Original Research articles and Reviews covering the following subtopics:
• Summary of current progression in biobank studies
• Studies conducted based on biobank data from multiple countries
• Benefits in biobank data research
• Summary of current integrated analysis of multiple-omics data
• Integrative analysis of multiple-omics data with consideration of biological structure
• Review of methodology development in integrative analysis of multiple-omics data
• Opportunities and challenges when combining biobank data from multiple countries
In recent decades, the advancements in high-throughput genomics technology allowed for cost and time affordable large-scale research projects. As a matter of fact, several governments around the world have initiated biobank projects to investigate the genetic profiles of their citizen and to have a long-term follow-up of the health data at a population level. The major goal of such biobank projects is to elucidate the etiology of diseases and to establish the linkage between phenotypes and genetic profiles. Several studies have been conducted to achieve this goal. However, when analysing the massive data from these large-scale biobank projects, many challenges have arisen, including the curse of high dimensionality and the difficulty in incorporating the biological structure in the genetics data.
The first goal of this Research Topic is to provide a comprehensive overview of the current advances in scientific and clinical knowledge deriving from biobank data analysis. We welcome algorithms and statistical models performing an integrated analysis of multiple-omics data in health and disease, with the aim to establish an association between the genetic profiles and biological phenotypes. The second goal of this collection is to discuss the difficulties and challenges of biobank data analysis. For example, the genetics data, such as DNA sequence variant, DNA copy number and DNA methylation, have a hierarchy and thus the incorporation of the biological structure in the analysis protocols may be crucial.
We welcome Original Research articles and Reviews covering the following subtopics:
• Summary of current progression in biobank studies
• Studies conducted based on biobank data from multiple countries
• Benefits in biobank data research
• Summary of current integrated analysis of multiple-omics data
• Integrative analysis of multiple-omics data with consideration of biological structure
• Review of methodology development in integrative analysis of multiple-omics data
• Opportunities and challenges when combining biobank data from multiple countries
