Reproducibility

Yes. The networks are precomputed from the underlying data, so querying the same genes with the same parameters in the same network will produce identical results every time. There is no stochastic element in the query process. The platform is retrieving a defined set of interactions from an established network model, not recalculating correlations in real time. Your results are fully reproducible within a given data version.

The only scenario where results would change is if the underlying network has been updated with new data or improved curation between your two queries. In that case, the difference reflects an improved data foundation, not inconsistency.

Any computational platform involves a chain of methodological decisions: normalization choices, correlation methods, thresholds, filtering criteria, etc. Our approach is to keep the science open and the engineering proprietary. The methodological foundations of the platform, or how networks are built, what statistical frameworks underpin the correlations, how quality control is performed, and how the models have been validated are documented and accessible through the Background Analyses and Approachesx-sections. We encourage you to review them and challenge them. Every methodological decision in the pipeline has been validated by our R&D team against established biological benchmarks.

What remains proprietary is the engineering and backend infrastructure: the platform architecture, optimization, and scaling systems that make it possible to build and serve these networks at the scale we do. This is our intellectual property, but it is distinct from the scientific methodology, which we believe should be open for review.

If you have specific questions about a particular methodological choice: why a certain threshold was used, how a normalization step was implemented, or how a validation was performed, our R&D team will be happy to discuss them directly.

We maintain version control across our data and network builds. Each network version is documented, including the data sources and database versions that contributed to it. This information is available through the platform.

For the specific software packages and internal pipeline tools used in the backend, these fall under our proprietary engineering infrastructure and are not disclosed at the package-level. However, the methodological approach including the statistical methods, normalization procedures, and validation frameworks is documented and transparent. If you need version-level detail for a specific publication or regulatory context, reach out to our team and we can discuss what can be provided on a case-by-case basis.

Our methodology is documented in the Background Analyses and Approachesx-section, which provides the information you need to describe the analytical approach in your own methods section, including the network construction framework, the data sources, and the statistical analyses.

To cite Mavatar Discovery, read more under Cite Mavatar Discoveryx. Here are some examples:

For more specific citation guidance, feel free to contact us directly. We're happy to help you draft the appropriate methods description for your manuscript and ensure it meets the reporting standards of your target journal.

To cite Mavatar Discovery, read more under Cite Mavatar Discoveryx. Here are some examples:

Network updates reflect improvements in data coverage, curation, and methodology. They are a feature of a growing platform, not a disruption to your work. Each network version is documented, and previous versions are retained within the platform, so you can still access and query older network builds if needed.

This means that if you ran an analysis and published results based on a specific network version, you can return to that same version for reproducibility. When a new version becomes available, you also have the option to rerun your analysis against the updated networks to see how expanded data or improved curation affects your results. If you need specific guidance on versioning for a publication or regulatory context, our team is happy to help.

This is a common concern when using any commercial platform in academic research, and it's not unique to Mavatar Discovery. The same applies to tools like Ingenuity Pathway Analysis (IPA), Metacore, or any licensed bioinformatics software.

In your manuscript, you describe the methodology and parameters used, cite the platform, and reference the documented methods available under Background Analyses and Approachesx. You are not expected to share proprietary source code any more than you would be expected to share the internal codebase of IPA or any other commercial tool. What matters for reproducibility is that the analytical approach is transparent, the parameters are reported, and the results are traceable, all of which the platform supports.

Regarding reviewer access, contact our team. We can discuss options for providing reviewers with temporary access to the platform so they can evaluate your analyses directly. We understand that peer review requires scrutiny, and we are prepared to support that process on a case-by-case basis.