Advances in high throughput biotechnology have enabled the use of omics-based methods in biological research. However, single-omics approaches provide a limited focus on the correlation between molecular alterations and phenotypes, but not the direction of the causal relationships in the biological systems. On the other hand, multi-omics approaches, using multi-layered omics data, can provide a broader insight into potential causal relationships between molecular alterations and biological phenotypes. Through multi-omics data analysis, information from genomic, transcriptomic, proteomic, lipidomic, and metabolomic levels can be integrated to provide a more reliable and holistic depiction of biological mechanisms.
Biological networks are important tools that describe interactions and regulatory mechanisms between biological macromolecules. Network-based methods are a powerful tool to model the biological processes, as such they are beneficial for studies in systems and computational biology. We can construct and utilize different biological networks using information from different omics data which represent different molecular layers. For example, combining gene regulatory networks, protein-protein interaction networks, and metabolic networks. Yet, it remains a challenge to integrate the multi-layer network in application to multi-omics data.
Complex diseases are recognized to be caused by the dysregulation of biological systems or molecular networks, rather than by the mutations of individual genes. Moreover, it is notoriously difficult and time-consuming to identify disease-related genes with biological experiments. This contrasts to many computational methods that have been presented to quickly predict disease-related genes, but most focus on the differential expression of genes while ignoring the regulation or network between molecules. Complex diseases are unlike single-gene disorders, and they do not have clear-cut patterns of inheritance; therefore, it is difficult to predict the risk of inheriting or passing on these diseases.
In this Research Topic, we look forward to the novel research in the field of multi-layered network analysis to identify potential biomarkers, disease genes, or uncover the mechanisms of complex disease development. Please note, studies relating to the prediction of clinical outcomes will not be considered for review without validation of findings. For example, commonly rejected submissions are re-analysis of existing genomic and transcriptomic data to identify a candidate set of biomarkers without validation.
Areas to be covered in this Research Topic may include, but are not limited to:
• New network-based analysis algorithms in multi-omics data;
• Application of network-based multi-omics analysis to complex diseases;
• Integration algorithms to analyze multi-omics data for complex disease;
• Identifying new network biomarkers by multi-omics integration;
• Detecting new functional modules from biological networks using multi-omics data.
Advances in high throughput biotechnology have enabled the use of omics-based methods in biological research. However, single-omics approaches provide a limited focus on the correlation between molecular alterations and phenotypes, but not the direction of the causal relationships in the biological systems. On the other hand, multi-omics approaches, using multi-layered omics data, can provide a broader insight into potential causal relationships between molecular alterations and biological phenotypes. Through multi-omics data analysis, information from genomic, transcriptomic, proteomic, lipidomic, and metabolomic levels can be integrated to provide a more reliable and holistic depiction of biological mechanisms.
Biological networks are important tools that describe interactions and regulatory mechanisms between biological macromolecules. Network-based methods are a powerful tool to model the biological processes, as such they are beneficial for studies in systems and computational biology. We can construct and utilize different biological networks using information from different omics data which represent different molecular layers. For example, combining gene regulatory networks, protein-protein interaction networks, and metabolic networks. Yet, it remains a challenge to integrate the multi-layer network in application to multi-omics data.
Complex diseases are recognized to be caused by the dysregulation of biological systems or molecular networks, rather than by the mutations of individual genes. Moreover, it is notoriously difficult and time-consuming to identify disease-related genes with biological experiments. This contrasts to many computational methods that have been presented to quickly predict disease-related genes, but most focus on the differential expression of genes while ignoring the regulation or network between molecules. Complex diseases are unlike single-gene disorders, and they do not have clear-cut patterns of inheritance; therefore, it is difficult to predict the risk of inheriting or passing on these diseases.
In this Research Topic, we look forward to the novel research in the field of multi-layered network analysis to identify potential biomarkers, disease genes, or uncover the mechanisms of complex disease development. Please note, studies relating to the prediction of clinical outcomes will not be considered for review without validation of findings. For example, commonly rejected submissions are re-analysis of existing genomic and transcriptomic data to identify a candidate set of biomarkers without validation.
Areas to be covered in this Research Topic may include, but are not limited to:
• New network-based analysis algorithms in multi-omics data;
• Application of network-based multi-omics analysis to complex diseases;
• Integration algorithms to analyze multi-omics data for complex disease;
• Identifying new network biomarkers by multi-omics integration;
• Detecting new functional modules from biological networks using multi-omics data.
