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About this Research Topic

Manuscript Submission Deadline 31 October 2022
Manuscript Extension Submission Deadline 30 November 2022

With extensively joint efforts and collaborations from various international institutions, last decade has witnessed an increasing development of large-scale high-throughput sequencing techniques. It benefits the research community a huge amount of valuable multi-omics data, which are continually generated at ever-growing rates. How to advance the systems biology findings with the data has been a challenging task. While recent sequencing technologies provide new potential for biomedical research, how to utilize cutting-edge in-silicon techniques, particularly the state-of-the-art machine learning, is critical to leverage the massive data. The ultimate goal is to understand the evolving and interacting mechanism and elucidate the complex process of organisms and disease development with the hierarchical interaction network(HINT) from multi-omics data at various scales.

This research topic therefore solicits state-of-the-art research work at the interface of these domains, with a focus on methods and addressing these challenges in the multi-omics era.

Areas to be covered in this Research Topic may include, but are not limited to:
- Methods that focus on the integration and interpretation of machine learning approaches on omics data for hierarchical interaction networks
- Comparative study of template-based and machine learning-based methods
- Multi-scale interaction network construction
- Methods that incorporate machine learning with HINT for the identification of secreted system effector, biomarkers, and drug targets

Keywords: HINT, Multi-omics data, methods, machine learning, interaction network, systems biology


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.

With extensively joint efforts and collaborations from various international institutions, last decade has witnessed an increasing development of large-scale high-throughput sequencing techniques. It benefits the research community a huge amount of valuable multi-omics data, which are continually generated at ever-growing rates. How to advance the systems biology findings with the data has been a challenging task. While recent sequencing technologies provide new potential for biomedical research, how to utilize cutting-edge in-silicon techniques, particularly the state-of-the-art machine learning, is critical to leverage the massive data. The ultimate goal is to understand the evolving and interacting mechanism and elucidate the complex process of organisms and disease development with the hierarchical interaction network(HINT) from multi-omics data at various scales.

This research topic therefore solicits state-of-the-art research work at the interface of these domains, with a focus on methods and addressing these challenges in the multi-omics era.

Areas to be covered in this Research Topic may include, but are not limited to:
- Methods that focus on the integration and interpretation of machine learning approaches on omics data for hierarchical interaction networks
- Comparative study of template-based and machine learning-based methods
- Multi-scale interaction network construction
- Methods that incorporate machine learning with HINT for the identification of secreted system effector, biomarkers, and drug targets

Keywords: HINT, Multi-omics data, methods, machine learning, interaction network, systems biology


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.

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