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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 457–480 of 2565 models
DNA language model combining Mamba state-space layers, gated dilated convolutions, and Fourier attention to capture multi-scale regulatory patterns.
Protein sequence embedding model, contrastively fine-tuned from ESM-2, that places functionally and structurally related proteins close together.
Enzyme function prediction that scores whether two sequences catalyze the same reaction, via attention pooling over frozen ESM Cambrian embeddings.
Microbiome foundation models that treat microbial community composition as a language, enabling zero- and few-shot transfer across prediction tasks.
All-atom protein co-design model that generates sequence and structure together in one unified diffusion process, aimed at hard binder design.
Three fixed ProtGPT2 fine-tunes specialized for metalloprotein generation, trained on ProteinMPNN-derived synthetic sequences.
Protein language model aligning ESM sequence embeddings with molecular dynamics trajectories for zero-shot mutation effect and stability prediction.
Causal multimodal transformer that embeds the do-operator in attention to predict single-cell gene expression under unseen genetic perturbations.
Photoplethysmography foundation model pretrained by reconstructing masked ECG from PPG, learning cardiac timing structure for wearable health tasks.
Autoregressive DNA foundation model for variant effect prediction, using 6-mer tokenization to match Evo2-7B win rates at far higher throughput.
Pathology report generation from whole-slide images, refreshing slide context and curated diagnostic concepts at every layer of the encoder.
Motif-aware graph diffusion model for controllable molecular generation that adapts to unseen properties by learning a lightweight task embedding.
Co-generative protein language model decoding sequence and structure tokens together from GO functional annotations for de novo protein design.
Reasoning-guided foundation model for de novo antibody CDR design, pairing a multimodal LLM understanding expert with a Boltz-1 diffusion expert.
Self-supervised brain MRI foundation model built on DINOv3, pretrained on roughly 6.6 million unlabeled axial slices for neuroimaging tasks.
Phylogeny-aware genome annotation model predicting exons, introns, UTRs and repeats directly from eukaryotic DNA, with no RNA or protein evidence.
Virtual spatial transcriptomics foundation model predicting pan-cancer, spatially-resolved single-cell gene expression from H&E histology slides.
Generative model for self-tracked menstrual health data, encoding cycles and symptom logs as token sequences for synthetic cohorts and forecasting.
Multi-modal transformer fusing LLM gene embeddings with biological knowledge graphs to predict single-cell responses to genetic perturbations.
Generative multimodal foundation model spanning DNA, RNA, and protein, with any-to-any inference across genome, transcriptome, and proteome.
Meta-learning framework that transfers perturbation responses across cell lines, donors, and drugs from a few measured seed perturbations.
Protein-protein docking model adapting AlphaFold-Multimer with a docking module and flow-matching training to assemble subunits without MSAs.
Direction-aware foundation model trained on bulk RNA-seq differential-expression profiles to simulate coordinated gene dynamics in viral infection.
110M-parameter RNA language model that designs sequences from secondary structure, motif, and Gene Ontology constraints via discrete diffusion.