There are 9 repositories under protein-function-prediction topic.
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
[ICLR 2022] OntoProtein: Protein Pretraining With Gene Ontology Embedding
Protein function prediction using a variational autoencoder
Deep Critical Learning. Implementation of ProSelfLC, IMAE, DM, etc.
DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction
Pipeline for searching and aligning contact maps for proteins, then running DeepFri's GCN.
Benchmarking uncertainty quantification methods on proteins.
Epistatic Net is an algorithm which allows for spectral regularization of deep neural networks to predict biological fitness functions (e.g., protein functions).
Domain-PFP is a self-supervised method to predict protein functions from the domains
PrimaryOdors.org molecular docker.
Assigns short human readable descriptions to biological sequences or gene families using references. For this, prot-scriber consumes sequence similarity search results in tabular format (Blast or Diamond).
python package to encode protein using different methods for machine learning
This repository contains the FPredX models for the prediction of excitation maximum, emission maximum, brightness and oligomeric state of fluorescent proteins.
Classification of protein function based on their sequences with Artificial Neural Networks
A nextflow pipeline to cluster sets of proteins.
Functions for converting Alphafold PDB molecules into graph representations for use with graph networks.
Integrating multimodal data through heterogeneous ensembles
🌱🌟 My Personal Portfolio 🌟🌱
Pipeline for searching and aligning contact maps for proteins, then running DeepFri's GCN. This repository is for portfolio purposes only. For currently maintained version go to Małopolskie Centrum Biotechnologii repository - https://github.com/bioinf-mcb/Metagenomic-DeepFRI
python package to train CNN and DenseNet for protein function prediction
Developing assembled functional classifications models via optimized machine learning algorithms
An efficient attention-based approach for Protein Function Prediction using skip-gram features. Proposing two novel approaches, namely, OntoPred and OntoPredPlus capable to annotate protein sequences accurately.
Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required.we expect to create an accurate prediction model that assigns the best sub-graph of the gene ontology to each new protein and output a prediction score for this sub-graph and/or each predicted term .
🧬Protein Functions Prediction through Amino Acids Sequences🧬