Repository Index

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2

• pytorch
• protein-structure
• alphafold2

Chai-1, SOTA model for biomolecular structure prediction

Deep probabilistic analysis of single-cell and spatial omics data

• scrna-seq
• variational-bayes
• variational-autoencoder
• cite-seq

Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis

Python package for graph neural networks in chemistry and biology

• deep-learning
• graph-neural-networks
• dgl
• cheminformatics

In-memory nucleotide sequence k-mer counting, filtering, graph traversal and more

• dna
• k-mer
• python
• bloom-filter

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

A tool for semi-automatic cell type classification

• python
• single-cell
• machine-learning
• scrna-seq

RNA Velocity generalized through dynamical modeling

Graph-linked unified embedding for single-cell multi-omics data integration

• bioinformatics
• deep-learning
• single-cell
• single-cell-multiomics

Benchmarking analysis of data integration tools

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

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.

Accurate and efficient spot detection for microscopy data

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

code to run sei and obtain sei and sequence class predictions

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

Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics

Multigrate: multiomic data integration for single-cell genomics

Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning