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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Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis
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
C++ library and cmdline tools for parsing and manipulating VCF files with python and zig bindings
- variant-calling
- genomics
- vcf-manipulation
- genotyping
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
- dna
- language-model
- variant-effect-prediction
- genomics
Bi-Directional Equivariant Long-Range DNA Sequence Modeling
🏃 The go-to single-cell Foundation Model
- batch-correction
- cell-biology
- celltype-annotation
- denoising
Structural variant detection and association testing
Toolbox for the analysis of smFISH images.
- pattern-classification
- rna-localization
- segmentation
- smfish
The framework for quantification of neuronal anatomy
- neuroscience
- neuroanatomy-toolbox
- imagej
- imagej2
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.
Analysis of cell migration in Igor Pro
- igor
- imagej
- cell-migration
- tracking
Vendor-agnostic, FHIR-Genomics-aligned API contract for cross-vendor NGS interpretation. JSON Schema + TypeScript + Python pydantic.