Making Protein folding accessible to all!
- bioinformatics
- multiple-sequence-alignment
- protein-structure
- structure-prediction
Repository Index
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An ultra-fast all-in-one FASTQ preprocessor (QC/adapters/trimming/filtering/splitting/merging...)
- fastq
- qc
- preprocessing
- filtering
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
- python
- ecg
- eda
- emg
Toolkit for processing sequences in FASTA/Q formats
- bioinformatics
- sequence-analysis
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
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
[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
UCE is a zero-shot foundation model for single-cell gene expression data
MOGONET (Multi-Omics Graph cOnvolutional NETworks) is a novel multi-omics data integrative analysis framework for classification tasks in biomedical applications.
CASSIA: A Multi-Agent LLM-Based Single-Cell Cell Type Annotation Framework
- ai4science
- llm
- multiagent-systems
- prompt-engineering
Toolbox for the analysis of smFISH images.
- pattern-classification
- rna-localization
- segmentation
- smfish
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.