All Competitors

Bayesian deep generative model that integrates gene regulatory networks into RNA velocity inference, enabling cell fate mapping and in silico perturbation of transcription factors.

A lightweight pan-cancer single-cell foundation model with a hybrid Transformer-Mamba architecture, released alongside the PanFoMaBench benchmark.

A BERT-style transformer language model built on the Geneformer framework and trained on zebrafish single-cell transcriptomics to produce gene and cell embeddings for developmental analysis.

Deep learning model predicting single-cell read counts from DNA sequence features and cell transition graphs to identify transcriptional regulators.

Machine learning framework for inferring cell-type-specific gene regulatory networks from single-cell multi-omics data and simulating transcription factor perturbations in silico.