All Competitors

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.

EEG foundation model pretrained by spectrogram reconstruction that improves online directional motor-imagery brain-computer interface control.

EEG-to-text foundation model that turns raw recordings into clinically grounded natural-language narratives instead of fixed-label classifications.

Generative transformer foundation model for continuous glucose monitoring, forecasting glycemia and stratifying health risk from raw glucose traces.

Single-lead ECG foundation model pretrained on 12-lead recordings, weighting contrastive pairs by clinical risk for cardiovascular risk prediction.

EEG foundation model whose learned queries map any electrode montage into a fixed latent space, scaling linearly in the number of channels.

fMRI foundation model of the human brain connectome: 1.2B parameters and brain-environment interaction tokens for behavior and disease prediction.

EEG foundation model for brain-computer interfaces, pairing masked pretraining with a mixture-of-experts transformer across electrode montages.

fMRI foundation model trained on cortical flat-map videos with masked autoencoding, showing power-law scaling on brain activity reconstruction.

Self-supervised bioacoustic audio encoder that turns animal-sound recordings into transferable embeddings for species classification and detection.

Surface-EMG wristband models that decode hand gestures, handwriting, and wrist movement, generalizing across users without per-person calibration.

Multimodal foundation model for cardiac biosignals, pretrained by masked modeling on ECG, PPG, and clinical text from ~1.7 million individuals.

Apple's foundation model trained on behavioral signals from wearables, modeling 27 HealthKit metrics to improve predictions across 57 health tasks.

Multi-scale ECG-language model that aligns 12-lead ECG signals with clinical text at token, beat, and rhythm levels for zero-shot cardiac diagnosis.

5.6M-parameter multimodal foundation model fusing fMRI time series with diffusion-MRI structural connectivity in a shared ROI embedding space.

Physiological signal foundation model for ECG, EMG, and EEG pairing learnable multi-scale wavelet decomposition with masked transformer pretraining.

Spatiotemporal foundation model that learns representations directly from 4D functional MRI volumes for disease diagnosis and phenotype prediction.

Sensor-language foundation models aligning wearable biosignals with text for zero-shot activity recognition, retrieval, and sensor captioning.

Multimodal clinical foundation model reasoning jointly over 2D and 3D medical images, ECG time-series, and text reports across nine clinical domains.

Brain foundation model unifying EEG and MEG in a single encoder via a shared discrete tokenizer that transfers across sensor layouts and montages.