Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–4 of 4 filtered models
Zhang Lab
Disentangled VAE framework for joint batch correction, condition-key-gene detection, and perturbation prediction in multi-batch multi-condition scRNA-seq data.
Kyoto University / Waseda University
A VAE-based generative model that designs novel functional RNA sequences by encoding MSA and consensus secondary structure constraints from Rfam families.
Xiamen University
End-to-end single-cell multimodal analysis framework using deep parametric inference to integrate RNA and protein data into a unified latent space.
Technical University of Denmark / University of Copenhagen
Variational autoencoder for single-cell RNA-seq that models raw count distributions directly, producing latent cell representations without normalization preprocessing.