In sequence-based predictions, conventionally an input sequence is represented by a multiple sequence alignment (MSA) or a representation derived from MSA, such as a position-specific scoring matrix. Recently, inspired by the development in natural language processing, several applications of sequence embedding have been observed. Here, we review different approaches of protein sequence embeddings and their applications including protein contact prediction, secondary structure, prediction, and function prediction.
@article{arxiv.2110.07609,
title = {Application of Sequence Embedding in Protein Sequence-Based Predictions},
author = {Nabil Ibtehaz and Daisuke Kihara},
journal= {arXiv preprint arXiv:2110.07609},
year = {2021}
}