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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 121–144 of 156 filtered models
Joint-embedding predictive foundation model pretrained on over a million unlabeled ECGs, learning transferable 12-lead representations for diagnosis.
Brain-dynamics foundation model for resting-state fMRI, adapting the Joint-Embedding Predictive Architecture with brain gradient positioning.
Multimodal neuroimaging model fusing resting-state fMRI and EEG, pretrained without labels across spatial, temporal, and spectral domains.
EEG foundation model whose dual objective aligns masked-patch features with whole-signal features, then transfers to new montages by linear probe.
EEG foundation model pretrained on 1.7TB of scalp and intracranial recordings, scaling attention across both time and channels to handle any montage.
Multimodal contrastive model aligning clinical EEG with free-text reports, enabling zero-shot EEG classification from natural-language prompts.
EEG-to-language foundation model that pairs a Q-Conformer encoder with a frozen LLM to decode coherent sentences from non-invasive brain recordings.
Multi-task EEG foundation model that treats brain signals as a foreign language, pairing a text-aligned neural tokenizer with a GPT-2 backbone.
Nanopore basecaller for fully 5-hydroxymethylcytosine-substituted DNA, reading raw ion current from strands that standard basecallers cannot resolve.
Neural spike decoding model whose Hebbian self-attention yields interpretable low-dimensional embeddings of electrophysiology and calcium imaging.
Multimodal ECG-language model aligning 12-lead waveforms with clinical report text for conversational cardiac diagnosis and report generation.
Foundation model for photoplethysmography (PPG) that learns quality-robust waveform representations for heart rate, blood pressure, and AF detection.
Open transformer foundation model for 12-lead electrocardiograms, pretrained on 1.5 million unlabeled ECGs with a wav2vec 2.0 self-supervised recipe.
Self-supervised foundation model for 12-lead ECG, pairing masked autoencoder pretraining with contrastive regularization for robust diagnostics.
Self-supervised masked autoencoder for functional MRI that learns representations from BOLD time-series with per-ROI embeddings and graph attention.
Respiratory acoustic foundation models pretrained on roughly 136K cough and breathing recordings for disease detection and lung function estimation.
EEG foundation model that learns transferable brain-signal representations with a vector-quantized tokenizer and masked transformer pretraining.
Multi-modal foundation model for sleep analysis, learning joint representations across brain, cardiac, and respiratory polysomnography signals.
ECG foundation model that learns 12-lead waveform representations by contrastively aligning each recording with machine-generated cardiological text.
Self-supervised CNN pretrained on 700,000 person-days of UK Biobank accelerometer data for human activity recognition across devices and cohorts.
Transformer neural speech decoder for intracranial ECoG and sEEG contacts at arbitrary cortical positions, generalizing to unseen participants.
Self-supervised foundation model for functional brain network analysis from resting-state fMRI, pretrained across 30 datasets for disorder diagnosis.
Foundation model spanning invasive SEEG/iEEG and non-invasive EEG in one backbone, with zero- and few-shot transfer across neurological disorders.