Official repository for the Boltz biomolecular interaction models
- binding-prediction
- drug-discovery
- protein-structure
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Official repository for the Boltz biomolecular interaction models
This package contains deep learning models and related scripts for RoseTTAFold
Chai-1, SOTA model for biomolecular structure prediction
Deep probabilistic analysis of single-cell and spatial omics data
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Biological foundation modeling from molecular to genome scale
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
Foundation Models for Genomics & Transcriptomics
Official implementation for HyenaDNA, a long-range genomic foundation model built with Hyena
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
Official release of the ProGen models
Generation of protein sequences and evolutionary alignments via discrete diffusion models
[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome
RNA Velocity generalized through dynamical modeling
GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations
Reference mapping for single-cell genomics
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
UCE is a zero-shot foundation model for single-cell gene expression data
Native R and Python sample-deletion robustness for omics: candidate certificates, minimum deletion bounds, Bioconductor model refits and donor-level pseudobulk
Schema and generated objects for biolink data model and upper ontology
Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell
Ankh: Optimized Protein Language Model