AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs
While the analysis of multi-omic datasets in a network context is poised to provide deeper understanding of health and disease, new methods are required to fully take advantage of this increasingly complex data. The current study combines several network analysis techniques into a single versatile method for analyzing biological networks with multi-omic data that can be applied in many diverse scenarios. Software is freely available in the R programming language at https://github.com/samboyd0/AMEND .
Authors
- Samuel S. Boyd (ORCID: https://orcid.org/0000-0002-0025-7100)
- Chad Slawson (ORCID: https://orcid.org/0000-0002-6886-4847)
- Jeffrey Thompson (ORCID: https://orcid.org/0000-0002-0876-2582)
Institutions
- University of Kansas (US)
- The University of Kansas Cancer Center (US)
- University of Kansas Medical Center (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-02-05
- DOI
- https://doi.org/10.1186/s12859-025-06063-x
- Citations
- 1
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- Georgia Clinical and Translational Science Alliance
- National Institutes of Health
- Frontiers Clinical and Translational Science Institute, University of Kansas
- National Cancer Institute
- National Center for Advancing Translational Sciences