Superzchen / iFeature
iFeature is a comprehensive Python-based toolkit for generating various numerical feature representation schemes from protein or peptide sequences. iFeature is capable of calculating and extracting a wide spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. Furthermore, iFeature also integrates…
☆190Updated 2 years ago
Alternatives and similar repositories for iFeature:
Users that are interested in iFeature are comparing it to the libraries listed below
- A Python-based Effective Feature Generation Tool from DNA, RNA, and Protein Sequences☆94Updated 2 years ago
- A software package for computing features of peptides and proteins☆58Updated 9 months ago
- Multi-task and masked language model-based protein sequence embedding models.☆99Updated 3 years ago
- Deep functional residue identification☆316Updated 2 years ago
- Protein sequence classification with self-supervised pretraining☆82Updated 3 years ago
- Modelling the Language of Life - Deep Learning Protein Sequences☆119Updated 4 years ago
- iLearn, a Python Toolkit and Web Server Integrating the Functionality of Feature Calculation, Extraction, Clustering, Feature Selection, …☆81Updated 2 years ago
- Evolutionary couplings from protein and RNA sequence alignments☆260Updated last month
- Install alphafold on the local machine, get out of docker.☆101Updated 3 years ago
- MXfold2: RNA secondary structure prediction using deep learning with thermodynamic integration☆124Updated 5 months ago
- iLearnPlus is the first machine-learning platform with both graphical- and web-based user interface that enables the construction of auto…☆106Updated 11 months ago
- Bilingual Language Model for Protein Sequence and Structure☆228Updated 3 months ago
- Modelling the Language of Life - Deep Learning Protein Sequences☆72Updated 4 years ago
- Official repository for the paper "Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning". Jo…☆167Updated last year
- Predicting direct protein-protein interactions with AlphaFold deep learning neural network models.☆159Updated 6 months ago
- predicting peptide-protein interactions☆128Updated last year
- Inference of couplings in proteins and RNAs from sequence variation☆108Updated 2 years ago
- Deep learning and Bayesian approach applied to enzyme turnover number for the improvement of enzyme-constrained genome-scale metabolic mo…☆151Updated last year
- DeepGO with GOPlus axioms☆91Updated 11 months ago
- ☆127Updated 4 years ago
- Universal Structure Alignment of Monomeric and Complex Structure of Nucleic Acids and Proteins☆129Updated 2 months ago
- RNA Secondary Structure Prediction using an Ensemble of Two-dimensional Deep Neural Networks and Transfer Learning.☆99Updated 2 years ago
- Efficient evolution from protein language models☆195Updated last year
- Python code for fine-tuning AlphaFold to perform protein-peptide binding predictions☆148Updated last year
- Antibody Numbering and Antigen Receptor ClassIfication☆206Updated 10 months ago
- ☆194Updated 3 years ago
- Protein Residue-Residue Contacts from Correlated Mutations predicted quickly and accurately.☆107Updated last year
- A generative latent variable model for biological sequence families.☆214Updated 3 years ago
- ☆104Updated last year
- ☆260Updated this week