Research Topic

Machine Learning and Network-Driven Integrative Genomics

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About this Research Topic

Availability of data and analysis tools were critical in the foundation of complex networks. In the past decade, since the birth of this discipline, a robust conceptual framework known as network biology has emerged. Understanding the dimension and dynamic properties of biological data, including gene-gene ...

Availability of data and analysis tools were critical in the foundation of complex networks. In the past decade, since the birth of this discipline, a robust conceptual framework known as network biology has emerged. Understanding the dimension and dynamic properties of biological data, including gene-gene and protein-protein interactions, and metabolic networks and pathways can help elucidate the functional properties of cells which will eventually assist further in understanding its development and disease dynamics.

Rapid advances in high-throughput technologies have produced distinct biomedical data sets that can be analyzed using mathematical and statistical models including network science tools to decode interactions between functional molecules in living cells. Machine learning, on the other hand, can handle heterogeneous data in different ways such as naive Bayesian Network data integration, Tree-Based Methods (e.g., random forest), and penalized linear models (e.g., LASSO). ML-based omics analyses provide assorted integrative analysis of multiple omics data, by analyzing different omics layers together. The discipline of Network biology is rapidly emerging with most recent applications to personalized medicine.

We welcome a variety of article types including Original Research, Review, Brief Research Report, Hypothesis and Theory, Methods, Mini Review, Perspective, Systematic Review, and Technology and Code. The topics of interest include, but are not limited to:

1) Machine learning, statistical methods, and computational tools for building biological networks,
2) Network-based analysis of disease.


Keywords: Genomics, Integrative analysis, Systems Biology, Machine learning


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