Modeling aspects of the language of life through transfer-learning protein sequences

Background Predicting protein function and structure from sequence is one important challenge for computational biology. For 26 years, most state-of-the-art approaches combined machine learning and evolutionary information. However, for some applications retrieving related proteins is becoming too t...

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Vydáno v:	BMC bioinformatics Ročník 20; číslo 1; s. 723 - 17
Hlavní autoři:	Heinzinger, Michael, Elnaggar, Ahmed, Wang, Yu, Dallago, Christian, Nechaev, Dmitrii, Matthes, Florian, Rost, Burkhard
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 17.12.2019 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Algorithms Amino Acid Sequence Amino acids Analysis Artificial intelligence Artificial neural networks Big data Bioinformatics Biological evolution Biomedical and Life Sciences Computational biology Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Data management Databases, Nucleic Acid Databases, Protein Deep learning Evolution Information processing Language Language Modeling Learning algorithms Life Sciences Localization Localization prediction Machine Learning Machine Learning and Artificial Intelligence in Bioinformatics Machine learning for computational and systems biology Microarrays Microbiomes Natural Language Processing Neural networks Neural Networks, Computer Predictions Principles Protein structure Proteins Proteins - chemistry Proteomics Proteomics - methods Research Article Secondary structure Secondary structure prediction Sequence Analysis Sequence Embedding Sequences Structure-function relationships Syntax Textbooks Transfer Learning Localization prediction Transfer Learning Secondary structure prediction Language Modeling Sequence Embedding Machine Learning Deep Learning
ISSN:	1471-2105, 1471-2105
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