Deep probabilistic analysis of single-cell and spatial omics data
- scrna-seq
- variational-bayes
- variational-autoencoder
- cite-seq
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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
Generation of protein sequences and evolutionary alignments via discrete diffusion models
- discrete-diffusion
- generative-model
- multiple-sequence-alignment
- protein-sequences
Spatial Single Cell Analysis in Python
- single-cell-rna-seq
- single-cell-genomics
- spatial-transcriptomics
- spatial-analysis
Sequential regulatory activity predictions with deep convolutional neural networks.
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
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
MCP server for spatial transcriptomics analysis through natural language interfaces.
- bioinformatics
- cell-communication
- deconvolution
- machine-learning
A versatile single-cell cross-modality translation method via dual-aligned variational autoencoders
Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics
- bioinformatics
- cell-type-deconvolution
- computational-biology
- deconvolution
PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"