Development of simple fitness landscapes for peptides by artificial neural filter systems

DOI10.1007/BF00201426zbMath0828.92015OpenAlexW1986417360MaRDI QIDQ1899179

Johannes Schuchhardt, Gisbert Schneider, Paul Wrede

Publication date: 14 December 1995

Published in: Biological Cybernetics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00201426

zbMATH Keywords

fitness functionshydrophobicityperceptronsartificial neural filter systemsclassification of dipeptidesclassification of proteolytic cleavage-sites of protein precursor sequencessequence optimizationsequence-oriented peptide design

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Biochemistry, molecular biology (92C40)

Related Items

Directed molecular evolution by machine learning and the influence of nonlinear interactions

Cites Work

• Self-organized formation of topologically correct feature maps
• Multilayer feedforward networks are universal approximators
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• Approximation by superpositions of a sigmoidal function

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