DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
- kmer
- dnabert-model
- kmer-format
- gpu
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DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
Official release of the ProGen models
Generation of protein sequences and evolutionary alignments via discrete diffusion models
C++ library and cmdline tools for parsing and manipulating VCF files with python and zig bindings
OmegaFold Release Code
Lightweight, super fast C/C++ (& Python) library for sequence alignment using edit (Levenshtein) distance.
Spatial Single Cell Analysis in Python
Generative biopolymer design with rich conditioning.
A Python Package for Protein Dynamics Analysis
A tool for semi-automatic cell type classification
RNA Velocity generalized through dynamical modeling
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Graph-linked unified embedding for single-cell multi-omics data integration
Sequential regulatory activity predictions with deep convolutional neural networks.
A dependency-free cross-platform swiss army knife for PDB files.
Benchmarking analysis of data integration tools
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GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations
Reference mapping for single-cell genomics
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
The repository host gives no description.
UCE is a zero-shot foundation model for single-cell gene expression data
The repository host gives no description.