Abstract
One of the challenges in multi-omics data analysis for precision medicine is the efficient exploration of undiscovered molecular interactions in disease processes. We present Bio- MOBS, a workflow consisting of two data visualization tools integrated with an open-source molecular information database to perform clinically relevant analyses (https://github.com/ driesheylen123/BioMOBS). We performed exploratory pathway analysis with BioMOBS and demonstrate its ability to generate relevant molecular hypotheses, by reproducing recent findings in type 2 diabetes UK biobank data. The central visualisation tool, where data-driven and literature-based findings can be integrated, is available within the github link as well. BioMOBS is a workflow that leverages information from multiple data-driven interactive analyses and visually integrates it with established pathway knowledge. The demonstrated use cases place trust in the usage of BioMOBS as a procedure to offer clinically relevant insights in disease pathway analyses on various types of omics data.
Cite
CITATION STYLE
Heylen, D., Peeters, J., Aerts, J., Ertaylan, G., & Hooyberghs, J. (2023). BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation. PLoS ONE, 18(12 December). https://doi.org/10.1371/journal.pone.0295361
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.