@inproceedings{0f850de62d094015bdb2c5c7ca9a24a7,
title = "Evolutionary context-integrated deep sequence modeling for protein engineering",
abstract = "Protein engineering seeks to design proteins with improved or novel functions. Compared to rational design and directed evolution approaches, machine learning-guided approaches traverse the fitness landscape more effectively and hold the promise for accelerating engineering and reducing the experimental cost and effort.",
author = "Yunan Luo and Lam Vo and Hantian Ding and Yufeng Su and Yang Liu and Qian, {Wesley Wei} and Huimin Zhao and Jian Peng",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020 ; Conference date: 10-05-2020 Through 13-05-2020",
year = "2020",
doi = "10.1007/978-3-030-45257-5_30",
language = "English (US)",
isbn = "9783030452568",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "261--263",
editor = "Russell Schwartz",
booktitle = "Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings",
address = "Germany",
}