Learning protein constitutive motifs from sequence data

Statistical analysis of evolutionary-related protein sequences provides information about their structure, function, and history. We show that Restricted Boltzmann Machines (RBM), designed to learn complex high-dimensional data and their statistical features, can efficiently model protein families f...

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Veröffentlicht in:	eLife Jg. 8
Hauptverfasser:	Tubiana, Jérôme, Cocco, Simona, Monasson, Rémi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England eLife Science Publications, Ltd 12.03.2019 eLife Sciences Publications Ltd eLife Sciences Publication eLife Sciences Publications, Ltd
Schlagworte:	Algorithms Amino Acid Motifs Amino acid sequence Amino acid sequencing Amino acids Applications Benchmarking Biochemistry Biochemistry, Molecular Biology coevolution Computational and Systems Biology Computational biology Computational Biology - methods Escherichia coli - chemistry Escherichia coli Proteins - chemistry Family relations Genetic Association Studies Genotypes Heat shock proteins HSP70 Heat-Shock Proteins - chemistry Hsp70 protein Humans Life Sciences Ligands Machine Learning Mathematical models Methods Models, Statistical Mutagenesis Normal Distribution Phenotypes Phylogeny Physics of Living Systems Probability Protein Binding Protein families Protein Interaction Mapping Protein Structure, Secondary Proteins Proteins - chemistry Proteins - genetics Quantitative Methods sequence analysis Statistical analysis Statistics Structure-function relationships Tools and Resources United States computational biology systems biology coevolution sequence analysis none machine learning physics of living systems
ISSN:	2050-084X, 2050-084X
Online-Zugang:	Volltext
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