scPair: Boosting single cell multimodal analysis by leveraging single cell atlases and implicit feature selection
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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.
Vendor-agnostic, FHIR-Genomics-aligned API contract for cross-vendor NGS interpretation. JSON Schema + TypeScript + Python pydantic.
gateway for segmentation data to Rstats & visualization with ggplot2 - in development
- visualization
- fluorescence-data
- ggplot
DeepAEG: A model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies
Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.
GNN-based gene-selection deep learning model for drug response prediction