Introducing Bio|Mx: AI-empowered multi-omics data integration & exploration application
Leveraging multi-omics analyses, or the integration of data across various omics disciplines such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics, holds immense potential for drug discovery and diagnostics R&D.
Multi-omics analysis can help you build a more complete biological understanding
By providing comprehensive molecular profiles across different biological layers, multi-omics approaches can offer detailed insights into disease mechanisms, biomarker identification, and drug response prediction. While single omics studies are powerful to be sure, by definition they miss patterns and interactions that are truly multi-modal, spanning across the DNA, epigenetic, RNA, and protein levels. Adding in additional layers of omics data allows us to untangle these interactions and relationships, resulting in both scientific and financial advantages.
In other words, multi-omics analysis can provide a unified snapshot of the biological processes within a cell or tissue in a manner beyond what any single omics dataset can capture. This clearer, more complete overview of biological processes that multi-omics analysis provides can help identify important players and interactions that may be missed with single-omics analyses, as exemplified by the fact that multi-omics analysis can predict outcomes, such as disease progression, better than single omics analysis. This can then translates into an economic advantage: by using multi-omics analysis, you reach a high resolution in one shot.
How multi-omics analysis benefits drug discovery and diagnostics R&D
In drug discovery, multi-omics analysis can enable the identification of novel drug targets, elucidation of complex molecular pathways underlying disease states, and development of data-driven personalized therapeutics tailored to individual patient profiles. Multi-omics analysis can also can inform drug repurposing efforts by identifying new indications for existing drugs based on shared molecular signatures across diseases.
Multi-omics analysis can also benefit diagnostics R&D by enabling the discovery of diagnostic biomarkers for early disease detection, improved (personalized) prognostics, and detailed treatment monitoring. Integrating multi-omics data with clinical information can also enhance diagnostic accuracy and enable precision medicine approaches, to ultimately improve patient outcomes.
Depending on your scientific question, using multi-omics analysis can get you to the same scientific insights quicker, translating into less time and money spent repeating experiments over the course of the project. On the other hand, multi-omics projects require skilled expertise to execute them well - which is why we built Bio|Mx to support scientists with multi-omics analysis.
What is Bio|Mx?
Bio|Mx is a pioneering multi-omics integration solution that combines public and proprietary omics datasets to provide you with comprehensive overviews of biological systems in a disease context. Leveraging advanced machine learning methods, combined with a powerful yet user friendly results exploration interface, Bio|Mx empowers scientists to create actionable insights.
What Bio|Mx offers you:
𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗰𝘂𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝗳 𝘀𝗶𝗻𝗴𝗹𝗲 𝗼𝗺𝗶𝗰𝘀 𝗱𝗮𝘁𝗮 𝘀𝗲𝘁𝘀
Through extensive literature and public repositories search using a combination of automated AI tools and review by BioLizard experts, Bio|Mx helps you to identify genomics, epigenomics, transcriptomics, proteomics and metabolomics data sets relevant for your project.
To ensure compatibility of all datasets, Bio|Mx performs automated harmonization of metadata across data sets, with manual curation by BioLizard experts.
To avoid data pollution by verifying that each omics dataset included is of sufficient quality, extensive single omics analysis of each individual data set is performed. This challenges and validates its fitness for purpose, using statistical performance metrics.
𝗠𝘂𝗹𝘁𝗶-𝗼𝗺𝗶𝗰𝘀 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻
Different omics data sets must be merged into a single, consolidated gene feature matrix, suitable for downstream analytics. Bio|Mx provides a range of different integration strategies, with BioLizard experts choosing the optimal approach in function of the concrete data for your project.
𝗔𝗜-𝗯𝗮𝘀𝗲𝗱 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 & 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆
Bio|Mx provides a range of different discovery strategies, with BioLizard experts on-board to help you choose the optimal approach in function of the concrete data for your project and your research questions.
For example:
- Unsupervised AI-based modelling, which involves de novo discovery without relying on any prior knowledge about the disease
- Supervised discovery, which leverages already known disease causal genes to boost the performance of the discovery process, e.g. with respect to causality
- Bio|Mx also leverages knowledge about protein-protein interaction networks to boost the discovery performance
𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻
To empower you to explore your results, combine them with other knowledge, and generate insights, Bio|Mx results are integrated in Bio|Verse, our groundbreaking framework for self-service bioinformatics citizen data science.
Bio|Verse offers:
- The combination of advanced bioinformatics plots with powerful visual analytics framework to allow you to drill down on promising subsets of results
- Collaborative tools that allow sharing of results
- Easy comparison of Bio|Mx results to other data in the Bio|Verse framework, including using Bio|Mx results as the basis for AI-empowered literature searches