Predicting the disulfide bonding state of cysteines with combinations of kernel machines

DOI10.1023/B:VLSI.0000003026.58068.cezbMath1042.68647OpenAlexW2139400169MaRDI QIDQ1430038

Alessio Ceroni, Andrea Passerini, Alessandro Vullo, Paolo Frasconi

Publication date: 27 May 2004

Published in: Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/b:vlsi.0000003026.58068.ce

zbMATH Keywords

machine learningkernel machinesstructural genomicsbonding state of cysteines

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies and applications (68U99)

Related Items (3)

Cooperativity of the oxidization of cysteines in globular proteins ⋮ Predicting the state of cysteines based on sequence information ⋮ Comparison of relevance learning vector quantization with other metric adaptive classification methods

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