The language of proteins: NLP, machine learning & protein sequences

Natural language processing (NLP) is a field of computer science concerned with automated text and language analysis. In recent years, following a series of breakthroughs in deep and machine learning, NLP methods have shown overwhelming progress. Here, we review the success, promise and pitfalls of...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computational and structural biotechnology journal Jg. 19; S. 1750 - 1758
Hauptverfasser: Ofer, Dan, Brandes, Nadav, Linial, Michal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.01.2021
Research Network of Computational and Structural Biotechnology
Elsevier
Schlagworte:
amino acids
Artificial neural networks
automation
Bag of words
BERT
Bioinformatics
biotechnology
computer science
Contextualized embedding
Deep learning
Language models
Natural language processing
Review
Tokenization
Transformer
Word embedding
Word2vec
Deep learning
Contextualized embedding
Word2vec
Bag of words
Transformer
Artificial neural networks
Natural language processing
BERT
Tokenization
Word embedding
Language models
Bioinformatics
ISSN:2001-0370, 2001-0370
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Natural language processing (NLP) is a field of computer science concerned with automated text and language analysis. In recent years, following a series of breakthroughs in deep and machine learning, NLP methods have shown overwhelming progress. Here, we review the success, promise and pitfalls of applying NLP algorithms to the study of proteins. Proteins, which can be represented as strings of amino-acid letters, are a natural fit to many NLP methods. We explore the conceptual similarities and differences between proteins and language, and review a range of protein-related tasks amenable to machine learning. We present methods for encoding the information of proteins as text and analyzing it with NLP methods, reviewing classic concepts such as bag-of-words, k-mers/n-grams and text search, as well as modern techniques such as word embedding, contextualized embedding, deep learning and neural language models. In particular, we focus on recent innovations such as masked language modeling, self-supervised learning and attention-based models. Finally, we discuss trends and challenges in the intersection of NLP and protein research.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
content type line 23
ISSN:2001-0370
2001-0370
DOI:10.1016/j.csbj.2021.03.022