Research Topic

Feature Synthesis and Interpretation for Multi-Omics Data Integration

About this Research Topic

With growing omics data being generated, powerful data-driven computational methods are urgently needed. Machine learning has been widely applied in analyzing these biological data, including sequencing data and interaction network data. How to generate and extract discriminant features is crucial for ...

With growing omics data being generated, powerful data-driven computational methods are urgently needed. Machine learning has been widely applied in analyzing these biological data, including sequencing data and interaction network data. How to generate and extract discriminant features is crucial for subsequent tasks. In addition, most machine learning involves black-box methods. Interpretability is crucial for analyzing biological data and understanding biological processes. Recently, deep feature synthesis and network embedding methods have been widely used. Considering that genes generally function through interacting with others, it bears important practical utility for learning representation from the interaction data for follow-up tasks and end-to-end graph neural network for biological tasks, and it also calls for robust model interpretability to explain how a model makes a prediction.

This Research Topic will bring together the state-of-the-art research contributions that include feature synthesis from multi-omics data and interpretable rule learning for explaining how a model makes a prediction in bioinformatics application. All submitted articles will be peer-reviewed and selected on the basis of their quality and relevance to the theme of this collection.

The subtopics of interest include, but are not limited to:
• Deep feature synthesis for multi-omics data.
• Integration of biological motivation into network embedding algorithms.
• Unsupervised network embedding on the interaction network.
• Graph convolution network for biological data analysis.
• Rule learning on large-scale biological data
• Model regulatory networks using multi-omics data.

Topic Editor Xi Wang is employed by The BASF Corporation (Ghent, Belgium). All other Topic Editors declare no competing interests with regard to the Research Topic subject.


Keywords: multi-omics, deep learning, graph neural network, feature synthesis, graph embedding


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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Submission Deadlines

22 May 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

22 May 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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