- Home
- Publications
- Publication Search
- Publication Details
Title
Advances in machine learning for directed evolution
Authors
Keywords
-
Journal
CURRENT OPINION IN STRUCTURAL BIOLOGY
Volume 69, Issue -, Pages 11-18
Publisher
Elsevier BV
Online
2021-02-27
DOI
10.1016/j.sbi.2021.01.008
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Deep Dive into Machine Learning Models for Protein Engineering
- (2020) Yuting Xu et al. Journal of Chemical Information and Modeling
- Signal Peptides Generated by Attention-Based Neural Networks
- (2020) Zachary Wu et al. ACS Synthetic Biology
- A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences
- (2020) Johannes Linder et al. Cell Systems
- Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design
- (2020) Brian Hie et al. Cell Systems
- Machine-learning-guided directed evolution for protein engineering
- (2019) Kevin K. Yang et al. NATURE METHODS
- Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics
- (2019) Claire N. Bedbrook et al. NATURE METHODS
- Unified rational protein engineering with sequence-based deep representation learning
- (2019) Ethan C. Alley et al. NATURE METHODS
- Deciphering protein evolution and fitness landscapes with latent space models
- (2019) Xinqiang Ding et al. Nature Communications
- Machine Learning in Enzyme Engineering
- (2019) Stanislav Mazurenko et al. ACS Catalysis
- Learned protein embeddings for machine learning
- (2018) Kevin K Yang et al. BIOINFORMATICS
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
- (2018) Tom Young et al. IEEE Computational Intelligence Magazine
- ProtaBank: A repository for protein design and engineering data
- (2018) Connie Y. Wang et al. PROTEIN SCIENCE
- Deep generative models of genetic variation capture the effects of mutations
- (2018) Adam J. Riesselman et al. NATURE METHODS
- Design of metalloproteins and novel protein folds using variational autoencoders
- (2018) Joe G. Greener et al. Scientific Reports
- UniProt: a worldwide hub of protein knowledge
- (2018) NUCLEIC ACIDS RESEARCH
- BRENDA in 2019: a European ELIXIR core data resource
- (2018) Lisa Jeske et al. NUCLEIC ACIDS RESEARCH
- The Pfam protein families database in 2019
- (2018) Sara El-Gebali et al. NUCLEIC ACIDS RESEARCH
- Extending the application of biocatalysis to meet the challenges of drug development
- (2018) Paul N. Devine et al. Nature Reviews Chemistry
- Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics
- (2015) Ehsaneddin Asgari et al. PLoS One
- Deep mutational scanning: a new style of protein science
- (2014) Douglas M Fowler et al. NATURE METHODS
- Navigating the protein fitness landscape with Gaussian processes
- (2013) P. A. Romero et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Engineering the third wave of biocatalysis
- (2012) U. T. Bornscheuer et al. NATURE
- Exploring protein fitness landscapes by directed evolution
- (2009) Philip A. Romero et al. NATURE REVIEWS MOLECULAR CELL BIOLOGY
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now