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
The code for using the CodonFM model checkpoint is available in the official Github
The code for using the CodonFM model checkpoint is available in the official Github
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM)
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM)
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM)
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM)
Evo 2 is a state-of-the-art DNA language model trained autoregressively on trillions of DNA
Evo 2 is a state-of-the-art DNA language model trained autoregressively on trillions of DNA
Evo 2 is a state-of-the-art DNA language model trained autoregressively on trillions of DNA
Using Caduceus To use the pre-trained model for masked language modeling, use the following snippet: python from transformers import AutoModelForMaskedLM,
Using Caduceus To use the pre-trained model for masked language modeling, use the following snippet: python from transformers import AutoModelForMaskedLM,
Chai-1 is a multi-modal foundation model for molecular structure prediction that performs at the state-of-the-art across a variety of benchmarks.
Boltz-1 is an open-source model which predicts the 3D structure of proteins, rna, dna and small molecules; it handles modified residues, covalent ligands and…
Mirror of the base ProGen2-base model (with slightly modified configuration and forward pass) by Nijkamp, et
Mirror of the base ProGen2-small model (with slightly modified configuration and forward pass) by Nijkamp, et
AMPLIFY is an efficient, state-of-the-art protein language model pre-trained using masked language modeling on UniRef100, OAS, and SCOP (UR100P).
AMPLIFY is an efficient, state-of-the-art protein language model pre-trained using masked language modeling on UniRef100, OAS, and SCOP (UR100P).
Model artifact mirrored from the HuggingFace Hub.
SMAFormer: Synergistic Multi-Attention Transformer for Medical Image
Model artifact mirrored from the HuggingFace Hub.
Model artifact mirrored from the HuggingFace Hub.
This model performs both segmentation and classification on chest radiographs (X-rays).
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
Model description - Intended uses and limitations - How to use - Limitations and bias - Training - Evaluation - Additional information - Author - Contact…