Models
A lexical baseline that scores a biomedical abstract against a fixed set of therapeutic areas.
A deterministic calculator rather than a learned model: average mass from an amino-acid sequence.
This is a convenient code wrapper to run Lundberg lab SubCell model in inference with your own images
a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
Message Passing Neural Networks for Molecule Property Prediction
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level.
GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations
State is a machine learning model that predicts cellular perturbation response across diverse contexts
Deep probabilistic analysis of single-cell and spatial omics data
A unifying representation of single cell expression profiles that quantifies similarity between expression states and generalizes to represent new studies…
UCE is a zero-shot foundation model for single-cell gene expression data
Source code repository mirrored from GitHub (biomap-research/scFoundation).
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
Channel-agnostic image encoding model CA-MAE with a ViT-S/16 encoder backbone designed for microscopy image featurization.
MoLFormer is a class of models pretrained on SMILES string representations of up to 1.1B molecules from ZINC and PubChem.
State Embedding model checkpoints are meant to be used with the Arc State