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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 169–192 of 237 filtered models
scVI variational autoencoder trained on the Tahoe-100M drug-perturbation atlas, giving a 10-dimensional embedding of treated cancer cell states.
Cell-free DNA methylation deconvolution at individual-read resolution, estimating cell-type proportions and condition-specific methylation profiles.
Neural ODE model of protein network dynamics, pretrained on 38 million perturbed protein measurements for drug efficacy and synergy prediction.
Single-cell foundation model for Drosophila that generates hierarchical cell-type annotations on new scRNA-seq datasets without any fine-tuning.
Single-cell ATAC-seq foundation model that builds cell representations from non-zero chromatin peaks via peak-to-gene alignment.
Spatial transcriptomics foundation model continually pretrained on 30 million profiles, with a protocol-aware mixture-of-experts decoder.
Perturbation representation model embedding CRISPR gene targets and small molecules in one space, transferring genetic screen models to drug response.
Gene regulatory network inference from single-cell or bulk RNA-seq with a graph transformer. One checkpoint transfers across species and cell types.
Spatial transcriptomics foundation model learning subcellular transcript positions and cell-niche context from 17 million Xenium single cells.
Single-cell analysis model driven by plain-language instructions, covering cell type annotation, pseudo-cell generation, and drug response prediction.
Single-cell foundation model pretrained by federated learning, modeling expression as a cell-by-gene table rather than an ordered gene sentence.
Graph neural network counting recurring cell-type neighborhood motifs in spatial transcriptomics and proteomics, linking topology to phenotype.
Multiomic foundation model for zero-shot in silico perturbation, predicting gene regulation and cell fate transitions from DNA and ATAC signal.
Single-cell epigenomic foundation model that reads scATAC-seq as cell sentences of accessible cCREs, pretrained on about 5 million human cells.
Hi-C foundation model pretrained on 118 million contact submatrices, fine-tuned for loop detection, resolution enhancement and epigenomic prediction.
Resolution enhancement for sparse single-cell Hi-C contact matrices, using a cascading residual GAN with self-attention over chromatin loci.
Single-cell RNA-seq representation model that separates batch-dependent from batch-independent variation to compare disease states across datasets.
Receptor activity inference from bulk or single-cell transcriptomes, reading the genes a receptor regulates instead of the receptor's own expression.