Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data.

ABSTRACT: Constantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo ( https://github.com/cellmapslab/kimono ). KiMONo performs network inference by using s

SUBMITTER: Ogris C 

PROVIDER: S-EPMC7990936 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature