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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 145–168 of 250 filtered models
Multimodal drug-response model coupling cell and molecule foundation models, pretrained on 1.8M perturbation RNA-seq profiles over 22,000 compounds.
Spatial omics foundation model that represents tissue as a hierarchical graph of neighboring cells over per-cell gene co-expression networks.
Reasoning LLM for single-cell type annotation, mapping per-cell expression to labels with marker-by-marker chains of thought on one GPU.
Distribution-level representation learning that embeds whole cell populations, perturbation responses, and sequence sets, not individual data points.
Transcriptomic perturbation prediction across unseen single and double gene knockdowns and unseen cell lines, driven by gene-gene knowledge graphs.
Single-cell chromatin accessibility foundation model with genome-aware tokenization, pretrained on 1.97 million scATAC-seq cells across 30 tissues.
Subject-level disease prediction from scRNA-seq, pairing cell-type-grouped scGPT pretraining with a Reactome pathway-constrained decoder.
Single-cell foundation model inferring cis-regulatory relationships from scRNA-seq and scATAC-seq, pretrained on an atlas of 1.3 million cells.
Generative single-cell foundation model trained on 112 million cells from 12 species, autoregressively modeling gene identities and expression counts.
Single-cell foundation model inferring context-specific protein-protein interactions from cancer transcriptomes via a variational graph autoencoder.
Single-cell type annotation pairing a CellMarker-derived knowledge graph with multi-agent LLM retrieval, generalizing across 11 tissue types.
Single-cell foundation model contrastively fine-tuned on genome-scale Perturb-seq data to separate perturbed from unperturbed transcriptomic states.
Single-cell foundation model built on bidirectional Mamba blocks and pretrained on 30 million cells for linear-time transcriptome embedding.
Multi-modal single-cell foundation model that projects Enformer DNA embeddings into a transcriptome model token space to predict gene regulation.
Multi-LLM consensus framework for automated cell type annotation in scRNA-seq data, outperforming prior methods by ~15% in mean accuracy.
Single-cell foundation model pre-trained on 50 million cells that infers cell-specific gene regulatory networks from transformer attention matrices.
Single-cell RNA-seq encoder trained with contrastive learning to merge plate- and droplet-based protocols, zero-shot on unseen tissues.
Spatial transcriptomics foundation model pretrained to generate a cell's expression profile from its neighbors, yielding zero-shot niche embeddings.
Single-cell foundation model reading scRNA-seq profiles as ranked gene-name sentences, scaled on Gemma-2 for annotation, reasoning and drug screens.
Single-cell multi-omics foundation model with a Mamba backbone, pretrained on 2.7 million paired scRNA-seq and scATAC-seq profiles.
Enhancer models predicting cell-type-specific chromatin accessibility from DNA sequence, with a pretrained zoo and synthetic enhancer design tools.
Multimodal graph foundation model fusing single-cell expression, biomedical text, and signaling networks, pretrained on ~80M sc/snRNA-seq profiles.
Genomic language model for scRNA-seq cell-type annotation, reweighting rare classes so diseased cell types are not swamped by common ones.
Single-cell RNA integration model using adversarial batch training to embed and label cells from a new study without supplying a batch ID.