02/02/2026 | Press release | Distributed by Public on 02/02/2026 10:12
A person's genetics do not change throughout their life. What does change, and does so constantly, is the scientific knowledge that allows us to interpret that information. This difference is forcing the healthcare and biotechnology sector to rethink how genetics are used in professional practice and how their long-term validity can be guaranteed.
Genetics has already established itself as a clinical tool for identifying predispositions and vulnerabilities in the field of health. A single genetic sample, obtained once, can provide relevant information for life. However, its usefulness depends on the ability to update its interpretation as scientific evidence advances.
Traditionally, genetic information has been delivered in the form of closed reports, based on the knowledge available at the time of analysis. Over time, these reports may become outdated as new studies are published, gene-phenotype associations are revised, or the understanding of certain biological processes is expanded, limiting their applicability in professional settings.
Given this scenario, the biotechnology sector is moving towards more dynamic models of genetic interpretation. N-GENE is a B2B genetics platform aimed at healthcare professionals that responds to this approach, allowing the interpretation of genetic predispositions to be kept up to date without the need to repeat the test.
The platform integrates artificial intelligence systems to support interpretation, with the aim of facilitating the continuous incorporation of new evidence and prioritising the most relevant information. This is organised into key points by areas such as nutrigenetics, pharmacogenetics, gynaecology, cardiovascular health, neurology, oncology, longevity and traumatology, allowing professionals to quickly access a structured and up-to-date overview.
In a healthcare system that is moving towards preventive, predictive and personalised medicine models, the ability to continuously update genetic interpretation becomes a determining factor in maximising the clinical value of a single genetic sample and facilitating a more efficient integration of these solutions into professional practice.