One off-the-shelf engine, every dataset
Multi-omics integration
Find cross-omic signatures, not single-layer hits.
Jointly analyze transcriptomics, methylomics, microbiome, and proteomics to surface biomarker combinations that single-omic analysis misses entirely.
Clinical & structured health records
Model tabular clinical and lab data directly.
Demographics, lab panels, medication history, and other structured EHR fields — combined or on their own, with no manual feature engineering.
Predictive classification
Classify subtypes, grades, and outcomes.
From cancer subtype and tumor grade to treatment response, get state-of-the-art classification with full explainability behind every call.
0.934 accuracy on multi-omic breast cancer subtype classification, ahead of the previous best-performing graph neural network approach — with full explainability included.
Radiomics & imaging-derived features
Predict from quantitative image features.
Radiomic and other feature sets extracted from medical images — no raw pixels required — to predict treatment response and outcomes.
Genomic sequence analysis
Decode DNA without a foundation model’s data bill.
Classify epigenetic marks, promoters, and regulatory motifs directly from sequence data — without massive pretraining corpora.
52% of genomic tasks won outright against pretrained DNA foundation models (DNABERT-2, NT-v2, HyenaDNA), using a fraction of a percent of their training data.
Small & imbalanced datasets
Get signal where other models see noise.
Rare-event prediction and small pilot cohorts are exactly the cold start cases traditional deep learning struggles with most.
2nd place in a public pediatric sepsis prediction competition against a highly imbalanced dataset, outperforming standard XGBoost approaches.
Discover trustworthy knowledge.
Trobant.AI Synthetic Cognition, a general-purpose, explainable AI engine for translational medicine and life sciences.
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