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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 145–168 of 287 filtered models
Multi-LLM consensus framework for automated cell type annotation in scRNA-seq data, outperforming prior methods by ~15% in mean accuracy.
Single-cell diaPASEF proteomics search that scores coelution with a pretrained CNN and returns a protein matrix with no missing values.
Cell type annotation from multiplexed tissue images, using a pretrained Vision Transformer ensemble that runs on new panels without fine-tuning.
Chemical perturbation model generating post-treatment transcriptomes for compounds and cell lines never screened, from SMILES structure and dose.
Single-cell foundation model for maize, pretrained on a 385,675-cell atlas with Gene Ontology priors for cell typing and cross-species transfer.
Single-cell foundation model running self-attention across all 27,874 human genes, with Gene Ontology priors injected through a graph network.
Single-cell RNA-seq representation model that separates batch-dependent from batch-independent variation to compare disease states across datasets.
Cross-species multimodal foundation model of immunology and inflammation, harmonizing transcriptomics and histology into patient-level embeddings.
Single-cell foundation model that predicts latent representations of graph-connected gene blocks instead of reconstructing individual gene counts.
Direction-aware foundation model trained on bulk RNA-seq differential-expression profiles to simulate coordinated gene dynamics in viral infection.
Single-cell metabolome inference from scRNA-seq, learned from spatially paired Visium and MALDI-MSI sections by multiple-instance learning.
Single-cell foundation model adding a gated cell-level contrastive objective to masked expression pretraining for transferable frozen cell embeddings.
Single-cell multi-omics foundation model with a Mamba backbone, pretrained on 2.7 million paired scRNA-seq and scATAC-seq profiles.
Self-supervised models that embed gut metagenomic abundance profiles for robust phenotype prediction in data-limited, cross-cohort settings.
Histology-anchored framework pairing an H&E foundation model with a cellular hypergraph to predict single-cell multi-omics from tissue images.
Histopathology foundation model predicting spatial gene expression from H&E slides at single-cell resolution via linear whole-slide attention.
Generative foundation model that imputes genes and denoises spatial transcriptomics, conditioned on H&E histology, scRNA-seq, and spatial priors.
Diffusion language model with 4.9 billion parameters that predicts genome-wide CRISPRi perturbation responses in single-cell transcriptomes.
Generative framework that learns a developmental vector field from scRNA-seq snapshots, coupling flow matching with molecular RNA kinetics.
Distribution-level representation learning that embeds whole cell populations, perturbation responses, and sequence sets, not individual data points.
Virtual cell model using masked discrete diffusion over the whole transcriptome to simulate scRNA-seq perturbation responses across tissues.
Single-cell type annotation pairing a CellMarker-derived knowledge graph with multi-agent LLM retrieval, generalizing across 11 tissue types.