Comprehensive understanding of human health and diseases requires interpretation of molecular intricacy, which is based on multi-omics data (e.g., SNPs, RNA expression and DNA methylation). With the rapid development of high-throughput omics technologies, researchers can measure multiple types of omics data, which can provide us with more comprehensive molecular insights. Hence, it is urgent to develop methods for integrating omics data from multiple platforms and to study the pathogenic mechanisms underlying complex diseases at various levels.
Besides, complex diseases are usually thought to be caused by disorders of biological systems or molecular networks, rather than a dysfunction of a single gene. Network analysis is an important tool for modeling the biological processes. Therefore, we are looking forward to building models which can integrate various networks generated by multi-omics data, for instance, combining gene regulatory networks, protein-protein interaction networks, and metabolic networks.
Moreover, although lots of statistical and machine learning methods have already studied the integrated analysis of multi-omics data or different networks generated by multi-omics data, these methods rarely analyze the causal relationship between complex diseases and multi-omics data. Understanding the causal relationships between genes and complex trait is a central goal of genetic study. Thus, we propose this Research Topic, and the subtopics include, but are not limited to:
- Novel statistical or machine learning methods for causal inference using multi-omics data.
- Novel statistical or machine learning methods for network analysis in multi-omics data.
- New algorithms, tools, pipeline, and databases for causal inference analysis.
- Application of multi-omics analysis to complex diseases.
Comprehensive understanding of human health and diseases requires interpretation of molecular intricacy, which is based on multi-omics data (e.g., SNPs, RNA expression and DNA methylation). With the rapid development of high-throughput omics technologies, researchers can measure multiple types of omics data, which can provide us with more comprehensive molecular insights. Hence, it is urgent to develop methods for integrating omics data from multiple platforms and to study the pathogenic mechanisms underlying complex diseases at various levels.
Besides, complex diseases are usually thought to be caused by disorders of biological systems or molecular networks, rather than a dysfunction of a single gene. Network analysis is an important tool for modeling the biological processes. Therefore, we are looking forward to building models which can integrate various networks generated by multi-omics data, for instance, combining gene regulatory networks, protein-protein interaction networks, and metabolic networks.
Moreover, although lots of statistical and machine learning methods have already studied the integrated analysis of multi-omics data or different networks generated by multi-omics data, these methods rarely analyze the causal relationship between complex diseases and multi-omics data. Understanding the causal relationships between genes and complex trait is a central goal of genetic study. Thus, we propose this Research Topic, and the subtopics include, but are not limited to:
- Novel statistical or machine learning methods for causal inference using multi-omics data.
- Novel statistical or machine learning methods for network analysis in multi-omics data.
- New algorithms, tools, pipeline, and databases for causal inference analysis.
- Application of multi-omics analysis to complex diseases.
