The AI engine Fit Assessment
Betae-NIOS leverages machine learning for genomic data analysis, enabling the discovery of new patterns and the development of novel therapeutics.
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Reproducible -omics data interpretation, i.e. the derivation of actionable molecular biomarkers, is currently the main bottleneck in streamlined applications, obstructing the transformation of genomic information into insights for Biomedicine and Biotech.
BioInfoMiner delivers unsupervized, fast and integrative interpretation of -omics experiments, in the form of biomarker signatures. Prioritized genes are mapped to few, clear, systemic pathways and phenotypes, enabling seamless biomarker identification and prioritization.