Official git repository for Biopython (originally converted from CVS)
- python
- bioinformatics
- genomics
- biopython
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Biological foundation modeling from molecular to genome scale
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.
Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
- kmer
- dnabert-model
- kmer-format
- gpu
[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome
- dataset
- dna
- dna-processing
- dna-training
SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.
Haplotype-aware CNV analysis from single-cell RNA-seq, ATAC-seq, and multiome
- cancer-genomics
- cnv-detection
- single-cell
- phylogeny
A Python package for the identification, characterization and comparison of spatial clusters from spatial -omics data.
Learning cell communication from spatial graphs of cells
- tensorflow
- gnn
- glm
- cvae
CASSIA: A Multi-Agent LLM-Based Single-Cell Cell Type Annotation Framework
- ai4science
- llm
- multiagent-systems
- prompt-engineering
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions
- computational-biology
- bioinformatics
- graph-convolution
- deep-learning
Python package for analysis of multiomic single cell RNA-seq and ATAC-seq.
Deep learning model for single-cell inference of multi-omic profiles from a single input modality.
- single-cell
- scrna-seq
- scatac-seq
- deep-neural-networks
Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network