Bio-basis function neural networks in protein data mining
- Author
- ZHENG RONG YANG1 ; HAMER, Rebecca2
[1] Department of Computer Science, University of Exeter, United Kingdom
[2] Division of Structural Biology, Oxford University, United States - Issue title
- Ground-breaking mathematical models for basic and applied research. Part-I
- Author (monograph)
- VASSILEV, Alexei O (Editor); TIBBLES, Heather E (Editor)
- Source
Current pharmaceutical design. 2007, Vol 13, Num 14, pp 1403-1413, 11 p ; ref : 75 ref
- ISSN
- 1381-6128
- Scientific domain
- Pharmacology drugs
- Publisher
- Bentham Science, Hilversum
- Publication country
- Netherlands
- Document type
- Article
- Language
- English
- Author keyword
- bio-basis function bioinformatics neural networks protein data mining systems biology
- Keyword (fr)
- Article synthèse Bioinformatique Biologie Fonction base radiale Fouille donnée Protéine Réseau neuronal
- Keyword (en)
- Review Bioinformatics Biology Radial basis function Data mining Protein Neural network
- Keyword (es)
- Artículo síntesis Bioinformática Biología Función radial base Busca dato Proteína Red neuronal
- Classification
- Pascal
- 002 Biological and medical sciences / 002A Fundamental and applied biological sciences. Psychology / 002A01 General aspects / 002A01B Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
- Discipline
- Biological sciences. Generalities. Modelling. Methods Generalities in biological sciences
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 18922386
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.2174/138161207780765927 
https://dx.doi.org/10.2174/138161207780765927
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