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  • Biochemistry & Molecular Biology - 2 - 2 - 106/290
Titulo: Naive Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer
Tipo: revista internacional
Fecha: 5,2012
Revista: Molecular Biosystems
JCR Journal; Impact Factor: 3.534
Citas ISI: 1 Citas Scopus: 8 Citas Google Scholar: 10
Volumen: Vol 8 (6)
Paginas: 1716-1722
ISSN: 1742-2051
Editorial: RSC Publishing
doi: 10.1039/c2mb25039j


Fast cancer diagnosis represents a real necessity in applied Medicine due to the importance of this disease. Thus, theoretical models can help as prediction tools. Graph theory representation is one option because permits to numerically describe any real system such as the protein macromolecules by transforming real properties in molecular graph topological indices. This study proposes a new classification model for proteins linked with human colon cancer by using spiral graph topological indices of protein amino acid sequences. The best quantitative structure-disease relationship model is based on eleven Shannon entropy indices. It was obtained with the Naďve Bayes method and shows excellent predictive ability (90.92%) for new proteins linked with this type of cancer. The statistical analysis confirms that this model allows diagnosing the absence of human colon cancer obtaining an area under receiver operating characteristic of 0.91. The methodology presented can be used for any type of sequential information such as any protein and nucleic acid sequence.\\h5-index (Google): 36

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