Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments
Repository Index
47,133 repositories indexed
1–17 of 17 · Most stars
A tool for semi-automatic cell type classification
- python
- single-cell
- machine-learning
- scrna-seq
Native R and Python sample-deletion robustness for omics: candidate certificates, minimum deletion bounds, Bioconductor model refits and donor-level pseudobulk
- bioinformatics
- exact-optimization
- metabolomics
- paired-data
Schema and generated objects for biolink data model and upper ontology
- biolink
- monarchinitiative
- gene-ontology
- datamodel
A Python package for the identification, characterization and comparison of spatial clusters from spatial -omics data.
scPerturb: A resource and a python/R tool for single-cell perturbation data
- cite-seq
- database
- perturbation
- scatac-seq
MOGONET (Multi-Omics Graph cOnvolutional NETworks) is a novel multi-omics data integrative analysis framework for classification tasks in biomedical applications.
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
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
Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications
- cell-annotation
- multiplexed-imaging
- mpif
The framework for quantification of neuronal anatomy
- neuroscience
- neuroanatomy-toolbox
- imagej
- imagej2
Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics
Deep learning model for single-cell inference of multi-omic profiles from a single input modality.
- single-cell
- scrna-seq
- scatac-seq
- deep-neural-networks
MCP server for spatial transcriptomics analysis through natural language interfaces.
- bioinformatics
- cell-communication
- deconvolution
- machine-learning
Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network
DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.