One off-the-shelf engine, every dataset
One off-the-shelf engine, applied across multiple life sciences use cases and datasets.
If it can be organized into rows and columns — regardless of where those numbers came from — Synthetic Cognition can learn from it. Some of the typologies we work with most, with public benchmark results shown where we have them:
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.
Found new worth exploring at wetlab functionally-consistent multi-omics signature patterns, not previously found with traditional methods
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.
Produces explainable prognoses and diagnoses
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 — 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.
Produces explainable prognoses and diagnoses
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's Synthetic Cognition is a general-purpose, explainable AI engine for translational medicine and life sciences.
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