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      Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information.

      1 , ,

      Bioinformatics (Oxford, England)

      Oxford University Press (OUP)

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          Abstract

          Human single nucleotide polymorphisms (SNPs) are the most frequent type of genetic variation in human population. One of the most important goals of SNP projects is to understand which human genotype variations are related to Mendelian and complex diseases. Great interest is focused on non-synonymous coding SNPs (nsSNPs) that are responsible of protein single point mutation. nsSNPs can be neutral or disease associated. It is known that the mutation of only one residue in a protein sequence can be related to a number of pathological conditions of dramatic social impact such as Alzheimer's, Parkinson's and Creutzfeldt-Jakob's diseases. The quality and completeness of presently available SNPs databases allows the application of machine learning techniques to predict the insurgence of human diseases due to single point protein mutation starting from the protein sequence.

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          Author and article information

          Journal
          Bioinformatics
          Bioinformatics (Oxford, England)
          Oxford University Press (OUP)
          1367-4811
          1367-4803
          Nov 15 2006
          : 22
          : 22
          Affiliations
          [1 ] Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna via Irnerio 42, 40126 Bologna, Italy.
          Article
          btl423
          10.1093/bioinformatics/btl423
          16895930

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