Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/42572
Title: BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation
Authors: HEYLEN, Dries 
PEETERS, Jannes 
AERTS, Jan 
Ertaylan, Gokhan
HOOYBERGHS, Jef 
Issue Date: 2023
Publisher: PUBLIC LIBRARY SCIENCE
Source: PLoS One, 18 (12) (Art N° e0295361)
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 BioMOBS, 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.
Notes: Heylen, D (corresponding author), Hasselt Univ, Data Sci Inst DSI, Theory Lab, Diepenbeek, Belgium.; Heylen, D (corresponding author), Flemish Inst Technol Res VITO, Mol, Belgium.
dries.heylen@vito.be
Keywords: Humans;Multiomics;Workflow;Software;Diabetes Mellitus, Type 2
Document URI: http://hdl.handle.net/1942/42572
ISSN: 1932-6203
e-ISSN: 1932-6203
DOI: 10.1371/journal.pone.0295361
ISI #: 001158744900061
Datasets of the publication: https://doi.org/10.1371/journal.pone.0295361.g001
Rights: 2023 Heylen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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