About this Research Topic

Manuscript Submission Deadline 30 November 2022

Graph mining techniques can provide a greater understanding of biological information. Conventional graph mining techniques learn graph structure from a node perspective by maximizing node pairwise relationships or simulating node information propagation in biological graphs. Graph neural network techniques can leverage the state-of-the-art deep learning techniques for problem solving and understanding of biological data. And knowledge graph techniques consider entity triplets from both the node and link perspectives to learn and predict biological graph structure.

Knowledge graphs in particular, make use of the known explicit and implicit connections of biological entities such as between genes, proteins, and drugs. These known relationships are used to form complex knowledge graphs integrating various data sources and linking known biological entities together. By applying graph model mining techniques and link prediction approaches on such knowledge graphs, further biological relationships can be revealed, which could potentially aid in the understanding and treatment of disease, the prediction of toxicity, and predicting compound and gene bioactivities.

Of note however are also the common challenges in graph mining which include but are not limited to node representation learning, node clustering, and link prediction. Addressing these challenges would add huge potential to biological research.

The goal of this research topic is to highlight graph mining applications to biological questions that integrate multiple data sources and/or help to address the common challenges faced in graph mining. Areas to be covered in this Research Topic may include, but are not limited to:
1. Integrative data and graph model mining methods/techniques that solve designed biological research questions.
2. Biological knowledge graph construction. New graph-based datasets are welcomed to construct by storing biological knowledge in complex graphs.
3. Empirical studies to compare the performance of different types of methods on certain biological research questions.

Please note: We expect the authors to share models and algorithms publicly and encourage authors to validate model predictions by comparing them to experimental data, including published experimental data.

Topic Editor Zheng Gao is employed by Amazon, US. All other Topic Editors declare no competing interests with regards to the Research Topic subject

Keywords: graph mining, knowledge graph, entity representation learning, link prediction, machine learning, systems biology, Systems Toxicology, Network Biology, Network Physiology


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.

Graph mining techniques can provide a greater understanding of biological information. Conventional graph mining techniques learn graph structure from a node perspective by maximizing node pairwise relationships or simulating node information propagation in biological graphs. Graph neural network techniques can leverage the state-of-the-art deep learning techniques for problem solving and understanding of biological data. And knowledge graph techniques consider entity triplets from both the node and link perspectives to learn and predict biological graph structure.

Knowledge graphs in particular, make use of the known explicit and implicit connections of biological entities such as between genes, proteins, and drugs. These known relationships are used to form complex knowledge graphs integrating various data sources and linking known biological entities together. By applying graph model mining techniques and link prediction approaches on such knowledge graphs, further biological relationships can be revealed, which could potentially aid in the understanding and treatment of disease, the prediction of toxicity, and predicting compound and gene bioactivities.

Of note however are also the common challenges in graph mining which include but are not limited to node representation learning, node clustering, and link prediction. Addressing these challenges would add huge potential to biological research.

The goal of this research topic is to highlight graph mining applications to biological questions that integrate multiple data sources and/or help to address the common challenges faced in graph mining. Areas to be covered in this Research Topic may include, but are not limited to:
1. Integrative data and graph model mining methods/techniques that solve designed biological research questions.
2. Biological knowledge graph construction. New graph-based datasets are welcomed to construct by storing biological knowledge in complex graphs.
3. Empirical studies to compare the performance of different types of methods on certain biological research questions.

Please note: We expect the authors to share models and algorithms publicly and encourage authors to validate model predictions by comparing them to experimental data, including published experimental data.

Topic Editor Zheng Gao is employed by Amazon, US. All other Topic Editors declare no competing interests with regards to the Research Topic subject

Keywords: graph mining, knowledge graph, entity representation learning, link prediction, machine learning, systems biology, Systems Toxicology, Network Biology, Network Physiology


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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