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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 25–48 of 156 filtered models
Intracortical speech brain-to-text decoder jointly pretrained across six BCI users, with over 50% lower relative word error than per-user models.
Calcium imaging foundation model for neural population forecasting and behavior decoding, with frozen-backbone transfer across three species.
EEG foundation model turning a short dry-electrode session into quantitative brain-function metrics for psychiatric and neurological assessment.
RNA modification profiling from nanopore direct RNA-seq signal; self-supervised pretraining resolves 11 modification types and extends to new ones.
Self-supervised foundation model for clinical flow cytometry, producing panel-agnostic specimen-level representations from multi-panel data.
Self-supervised flow cytometry model producing tube-level representations that lightweight heads read for hematologic diagnosis and sample viability.
Toxicity screening foundation model that encodes Tox21 concentration-response curves and assay metadata into reusable 768-dimensional embeddings.
Graph foundation model for fMRI brain networks, pretrained across 27 datasets with graph and language prompts for zero-shot disorder classification.
Self-supervised foundation model for continuous glucose monitoring, with dual streams separating slow physiological state from transient events.
EEG foundation model pretrained on 28,000+ hours of scalp recordings, with expert capacity allocated by measured layer-wise similarity.
Foundation model for paired ECG Lead-II and PPG waveforms, fusing the two through cross-modal attention and adaptive residual vector quantization.
Photoplethysmography foundation model pretrained by reconstructing masked ECG from PPG, learning cardiac timing structure for wearable health tasks.
Generative model for self-tracked menstrual health data, encoding cycles and symptom logs as token sequences for synthetic cohorts and forecasting.
Flow cytometry foundation model that reads heterogeneous antibody panels through a universal marker embedding to predict sample-level phenotypes.
EEG foundation model pretrained by spectrogram reconstruction that improves online directional motor-imagery brain-computer interface control.
Joint embedding predictive architecture for at-home polysomnography, encoding full-night seven-channel recordings for sleep staging and disease risk.
Voice biomarker model that flags current type 2 diabetes from one 20-second speech recording, validated against HbA1c blood tests in 801 adults.
EEG-to-text foundation model that turns raw recordings into clinically grounded natural-language narratives instead of fixed-label classifications.
ECG foundation model pretrained with a Split-Mask-Join masking objective and spatial reduction attention, aimed at diagnosis where labels are scarce.
Automated sleep staging for polysomnography in Parkinson's disease and isolated REM sleep behaviour disorder, with per-epoch confidence estimates.
Generative transformer foundation model for continuous glucose monitoring, forecasting glycemia and stratifying health risk from raw glucose traces.
Joint embedding predictive architecture for ICU bedside waveforms, learning frozen ABP, ECG and PPG representations for five-minute risk estimation.
Single-lead ECG foundation model pretrained on 12-lead recordings, weighting contrastive pairs by clinical risk for cardiovascular risk prediction.