ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
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Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments
Foundation Models for Genomics & Transcriptomics
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
- kmer
- dnabert-model
- kmer-format
- gpu
Spatial Single Cell Analysis in Python
- single-cell-rna-seq
- single-cell-genomics
- spatial-transcriptomics
- spatial-analysis
[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome
- dataset
- dna
- dna-processing
- dna-training
A tool for semi-automatic cell type classification
- python
- single-cell
- machine-learning
- scrna-seq
Schema and generated objects for biolink data model and upper ontology
- biolink
- monarchinitiative
- gene-ontology
- datamodel
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)
Haplotype-aware CNV analysis from single-cell RNA-seq, ATAC-seq, and multiome
- cancer-genomics
- cnv-detection
- single-cell
- phylogeny
Probabilistic cell segmentation for in situ spatial transcriptomics
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
- disentanglement
- drug-discovery
- genomics
- perturbation
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
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
BulkFormer: A large-scale foundation model for human bulk transcriptomes
Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics
MCP server for spatial transcriptomics analysis through natural language interfaces.
- bioinformatics
- cell-communication
- deconvolution
- machine-learning
The repository host gives no description.
Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network
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