Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence | bioRxiv
Deep Dive into Machine Learning Models for Protein Engineering | Journal of Chemical Information and Modeling
Embeddings from protein language models predict conservation and variant effects | SpringerLink
Sequence Embedding for Clustering and Classification | by Chitta Ranjan | Towards Data Science
Deep embedding and alignment of protein sequences | Nature Methods
Improved global protein homolog detection with major gains in function identification | PNAS
Conjoint Feature Representation of GO and Protein Sequence for PPI Prediction Based on an Inception RNN Attention Network - ScienceDirect
Can We Learn the Language of Proteins? – The Berkeley Artificial Intelligence Research Blog
Biomolecules | Free Full-Text | EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction
Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space | Scientific Reports
Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map | Journal of Cheminformatics | Full Text
Graph-based prediction of Protein-protein interactions with attributed signed graph embedding | BMC Bioinformatics | Full Text
Learning meaningful representations of protein sequences | Nature Communications
Molecule generator network is defined by combining LSTM model and... | Download Scientific Diagram
Learning the language of proteins
Using word embedding technique to efficiently represent protein sequences for identifying substrate specificities of transporters - ScienceDirect
Sequence Embedding for Clustering and Classification | by Chitta Ranjan | Towards Data Science
Application of Sequence Embedding in Protein Sequence-Based Predictions
Learning the molecular grammar of protein condensates from sequence determinants and embeddings | PNAS
Adaptive GDDA-BLAST: Fast and Efficient Algorithm for Protein Sequence Embedding | PLOS ONE
![An integration of deep learning with feature embedding for protein–protein interaction prediction [PeerJ]](https://dfzljdn9uc3pi.cloudfront.net/2019/7126/1/fig-2-2x.jpg "An integration of deep learning with feature embedding for protein–protein interaction prediction [PeerJ]")
An integration of deep learning with feature embedding for protein–protein interaction prediction [PeerJ]
Modeling aspects of the language of life through transfer-learning protein sequences | BMC Bioinformatics | Full Text
Frontiers | Predicting the Disease Risk of Protein Mutation Sequences With Pre-training Model
![PDF] Learning protein sequence embeddings using information from structure | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/3637fcccc758786ae1c6529ab22fe85ea98e9c36/2-Figure1-1.png "PDF] Learning protein sequence embeddings using information from structure | Semantic Scholar")
PDF] Learning protein sequence embeddings using information from structure | Semantic Scholar
The 4-step flowchart demonstrating our method for using word embedding... | Download Scientific Diagram
Deep learning with feature embedding for compound-protein interaction prediction | bioRxiv
![An integration of deep learning with feature embedding for protein–protein interaction prediction [PeerJ]](https://dfzljdn9uc3pi.cloudfront.net/2019/7126/1/fig-3-2x.jpg "An integration of deep learning with feature embedding for protein–protein interaction prediction [PeerJ]")