Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
- drug-discovery
- quantum-chemistry
- deep-learning
- biology
Repository Index
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DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
- tensorflow
- deep-neural-network
- genomics
- science
This package contains deep learning models and related scripts for RoseTTAFold
Deep probabilistic analysis of single-cell and spatial omics data
- scrna-seq
- variational-bayes
- variational-autoencoder
- cite-seq
Python package for graph neural networks in chemistry and biology
- deep-learning
- graph-neural-networks
- dgl
- cheminformatics
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
- kmer
- dnabert-model
- kmer-format
- gpu
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
- deep-learning
- protein-sequences
- protein-structure
- semi-supervised-learning
Official release of the ProGen models
- language-model
- protein
- generative-model
OmegaFold Release Code
A tool for semi-automatic cell type classification
- python
- single-cell
- machine-learning
- scrna-seq
Graph-linked unified embedding for single-cell multi-omics data integration
- bioinformatics
- deep-learning
- single-cell
- single-cell-multiomics
GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations
The repository host gives no description.
Open source tumor evidence matching for sequencing labs and hospitals without molecular tumor boards. Research prototype; oncologist review required.
- bioinformatics
- cancer-research
- computational-biology
- drug-repurposing
Open-source software for exploring and analyzing large, high-dimensional image-derived data.
- cellprofiler-analyst
- cellprofiler
- image-analysis
- machine-learning
TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524
- self-attention
- compound-protein-interaction
- transformercpi
- structure-free-virtual-sreening
Interpretable bilinear attention network with domain adaptation improves drug-target prediction.
A tool for cell instance aware segmentation in densely packed 3D volumetric images
- segmentation
- deep-learning
- bioinformatics
- neural-network
Learning cell communication from spatial graphs of cells
- tensorflow
- gnn
- glm
- cvae
Toolbox for the analysis of smFISH images.
- pattern-classification
- rna-localization
- segmentation
- smfish
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions
- computational-biology
- bioinformatics
- graph-convolution
- deep-learning
PyTorch implementation of the MIDAS algorithm for single-cell multimodal data integration (Nature Biotechnology 2024).
- deep-generative-model
- deep-learning
- single-cell-data-integration
- single-cell-multiomics
MCP server for spatial transcriptomics analysis through natural language interfaces.
- bioinformatics
- cell-communication
- deconvolution
- machine-learning
3D shape analysis using deep learning
- cancer-biology
- cell-biology
- deep-learning
- geometric-deep-learning