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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 73–96 of 156 filtered models
EEG foundation model whose codebook tokenizer encodes Fourier phase on the unit circle, gaining six points of balanced accuracy over LaBraM.
Brain foundation model unifying EEG and MEG in a single encoder via a shared discrete tokenizer that transfers across sensor layouts and montages.
EEG foundation model that separates channel-wise from temporal attention, pretrained on 25,000 hours of recordings spanning eight task paradigms.
Multimodal physiological foundation model spanning EEG, ECG, EOG, and EMG that keeps working when arbitrary modalities are missing at inference time.
Hypergraph foundation model for brain disease diagnosis from resting-state fMRI, self-supervised on high-order connectivity among brain regions.
Generative diffusion foundation model for human gait dynamics that estimates ground reaction forces from partial motion-capture kinematics.
Self-supervised transformer encoding a full eight-hour, seven-channel polysomnogram into task-agnostic representations for automated sleep staging.
EEG foundation model for clinical diagnosis, combining a VQ-VAE spectral tokenizer with masked token prediction for seizure and pathology detection.
Retinal encoding models that predict ganglion cell responses to visual stimuli, with pretrained checkpoints across four species and two modalities.
Multimodal LLM unifying 12-lead ECG time series, ECG images, and text for grounded, clinician-aligned electrocardiogram interpretation.
Brain-to-text decoder that reconstructs typed sentences from non-invasive MEG and EEG brain recordings using a CNN, transformer, and language model.
Single-channel EEG tokenizer that learns a discrete vocabulary of time-frequency motifs, usable as a front end for existing EEG foundation models.
ECG foundation model that treats heartbeats as words and rhythm strips as sentences, using heartbeat-level tokenization for diagnostic classification.
EEG foundation model pretrained by momentum contrastive learning and masked reconstruction, with a learnable channel mapping that unifies montages.
EEG foundation model built on bidirectional Mamba blocks that scale linearly with recording length, pretrained on 21,000 hours of clinical EEG.
Functional MRI foundation model that learns brain dynamics as a stochastic optimal control problem, self-supervised on 41,072 UK Biobank subjects.
Autoregressive transformer pretrained on 2,000 hours of intracortical spiking activity, decoding motor intent across subjects, species, and tasks.
Multimodal ECG language model pairing a specialized signal encoder with a biomedical LLM for cardiovascular disease detection and question answering.
Photoplethysmography foundation model pretrained on raw wearable signals from a field study, transferring across lab and field health tasks.
EEG foundation model for Alzheimer's disease detection, pretrained by contrastive learning across 13 clinical EEG datasets and 2,238 subjects.
Nanopore basecaller built on a Squeezeformer encoder, turning raw ion-current signal into DNA at 93.97% average read identity across 11 datasets.
Compact EEG foundation model whose alternating attention separates within-channel time from across-channel space, cutting attention memory sixfold.