Showing 7 of 7 projects
A scalable Python toolkit for RNA velocity analysis in single cells using dynamical modeling.
A 100M-parameter foundation model for single-cell transcriptomics, enabling gene expression enhancement, drug response prediction, and perturbation analysis.
A BERT-based foundation model pretrained on large-scale scRNA-seq data for automated cell type annotation in single-cell analysis.
A zero-shot foundation model for generating universal embeddings from single-cell gene expression data.
A deep convolutional neural network that predicts RNA-seq coverage at 32bp resolution from DNA sequence.
A pre-trained language model for single-cell RNA sequencing data that encodes cell-cell relations and accelerates inference for downstream tasks.
A large-scale foundation model pretrained on over 500,000 human bulk RNA-seq profiles for biomedical transcriptome analysis.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.