All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–18 of 18 filtered models
tf-SFM
—2—Transcription factor-DNA binding specificity prediction from sequence, with a physics-derived dual-encoder trained by symmetric contrastive learning.
DNA & Gene18Opennessdrug-SFM
—1—Specificity foundation model predicting small-molecule drug-target binding from sequence, scored as cross-modal retrieval without docking or assays.
Small molecule16Opennessmir-SFM
—2—Foundation model that predicts microRNA-mRNA target specificity from sequence, using a dual-encoder trained with a symmetric contrastive objective.
RNA25OpennessmhcSFM
—2—Peptide-MHC binding specificity model that frames presentation as cross-modal retrieval, aligning peptide and MHC encoders by contrastive learning.
Protein23OpennesscrisprSFM
—2—CRISPR off-target prediction model that scores gRNA-DNA specificity from sequence, framing guide-target recognition as cross-modal retrieval.
DNA & Gene19Opennessenzyme-SFM
—2—Enzyme-substrate specificity model that scores catalytic pairs from sequence with a physics-derived dual-encoder and a contrastive objective.
Protein23OpennessProtAlign
———Lawrence Livermore National LaboratoryMarch 6, 2026contrastive_learningcross_modal_retrievalembeddings+4Cross-modal protein encoder that aligns ESM-2 sequence embeddings with ProteinMPNN structure embeddings in a shared space for cross-modal retrieval.
Protein35OpennessNeuroVLM
8——Vision-language foundation model linking human brain activation maps and neuroscience text for text-to-brain and brain-to-text generation.
ImagingLanguage model74OpennessSIGMMA
—1—Helmholtz Munich +1 otherNovember 19, 2025contrastive_learningcross_modal_retrievalgene_expression_prediction+7Multi-modal contrastive model that aligns H&E histopathology with spatial transcriptomics across tissue scales to predict gene expression from images.
PathologySpatial omics20OpennessCLASP
44—Tri-modal contrastive model aligning protein structure, sequence, and text in a shared space for zero-shot cross-modal retrieval and classification.
Protein42OpennessEyeCLIP
8763—The Hong Kong Polytechnic University +5 othersJune 21, 2025clipcontrastive_learningcross_modal_retrieval+11CLIP-based vision-language foundation model for eye imaging, enabling zero-shot disease detection and cross-modal retrieval across 11 modalities.
ImagingLanguage model15OpennessSensorLM
———Google Research +2 othersJune 10, 2025activity_recognitioncontrastive_learningcross_modal_retrieval+6Sensor-language foundation models aligning wearable biosignals with text for zero-shot activity recognition, retrieval, and sensor captioning.
BiosignalsLanguage model41OpennessMUSK
239——Vision-language foundation model for precision oncology, pretrained on 50M pathology images and 1B text tokens via unified masked modeling.
PathologyLanguage model12OpennessWearable accelerometry foundation model distilled from a PPG encoder, predicting cardiovascular and health biomarkers from motion signals alone.
Biosignals5OpennessTITAN
356—134.7KSlide-level pathology foundation model turning whole-slide images into reusable embeddings for classification, retrieval, and report generation.
Pathology21Openness- Charité – Universitätsmedizin BerlinSeptember 11, 2024clinical_phenotypingcontrastive_learningcross_modal_retrieval+5
Multimodal contrastive model aligning clinical EEG with free-text reports, enabling zero-shot EEG classification from natural-language prompts.
BiosignalsLanguage model26Openness SleepFM
174——Multi-modal foundation model for sleep analysis, learning joint representations across brain, cardiac, and respiratory polysomnography signals.
Biosignals76OpennessT3D
—120—Vision-language pretraining for 3D CT volumes, aligning scans with their radiology reports for zero-shot classification, retrieval, and segmentation.
ImagingLanguage model12Openness