ashford-andrew/UniVI-PBMC-Multiome

!PIVOT overview

GeneJEPA is a Joint-Embedding Predictive Architecture (JEPA) trained for self-supervised representation learning on scRNA-seq. It uses a Perceiver-style encoder to handle sparse, high-dimensional gene count vectors and a Fourier-feature tokenizer for numerical tokenization.

Indus-Retriever (nasa-smd-ibm-st-v2) is a Bi-encoder sentence transformer model, that is fine-tuned from nasa-smd-ibm-v0.1 encoder model. it is an updated version of nasa-smd-ibm-st with better performance (shown below). It's trained with 271 million examples along with a domain-specific dataset of…

A frontier protein-language generative model — because proteins deserve better small talk.

SERAPH is a deep learning model designed for 3-state (Q3) protein secondary structure prediction. It processes raw single amino acid sequences and predicts residue-level secondary structure states: Alpha Helix (H), Beta Sheet (E), or Coil/Loop (C).

This model aims to be a base template for new models. It has been generated using this raw template.