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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–24 of 134 filtered models
EEG foundation model pairing masked contextual modelling with cross-view invariance learning over 11,000 hours of routine clinical recordings.
Bioacoustic encoder for birdsong that resolves individual syllables at 5 ms, using asymmetric spectrogram patches and Voronoi masked pretraining.
Self-supervised stereo-EEG encoder that localizes the seizure onset zone in drug-resistant epilepsy from peri-ictal superlet spectrograms.
Sleep staging from one behind-the-ear electrode pair, feeding automated REM-sleep-without-atonia scoring and REM sleep behaviour disorder detection.
Cardiac foundation model with one shared Transformer encoder for ECG, PPG, and PCG, aligning modalities in latent cardiac time via a learned delay.
Self-supervised single-lead ECG encoder pretrained on ten-minute ambulatory windows, giving patient-consistent embeddings for rhythm detection.
EEG foundation model that decodes by matching neural activity to label text embeddings, with one instruction-tuned checkpoint covering seven tasks.
ECG foundation model that reads any subset of the 12 standard leads natively, encoding recordings as variable-size spatiotemporal graphs.
EEG foundation model that corrects low-frequency bias by reconstructing band-standardized time-frequency targets. State of the art on 24 of 41 tasks.
EEG foundation model coupling spatial and temporal transformer branches through a shared soft mixture-of-experts, adapted by tuning 5.1% of weights.
EEG foundation model pretrained to predict structured latent states rather than masked waveforms, reaching 52.94% frozen macro balanced accuracy.
EEG foundation model for continuous monitoring, using windowed alternating attention to hold KV-cache memory constant on recordings up to 14 hours.
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.
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.
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.