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1.
Summary The 20 naturally occurring amino acids are characterized by 20 variables: pKNH 2, pKCOOH, pI, molecular weight, substituent van der Waals volume, seven1H and13C nuclear magnetic resonance shift variables, and eight hydrophobicity-hydrophilicity scales. The 20-dimensional data set is reduced to a few new dimensions by principal components analysis. The three first principal components reveal relationships between the properties of the amino acids and the genetic code. Thus the amino acids coded for by adenosine (A), uracil (U), or cytosine (C) in their second codon position (corresponding to U, A, or G in the second anticodon position) are grouped in these components. No grouping was detected for the amino acids coded for by guanine (G) in the second codon position (corresponding to C in the second anticodon position). The results show that a relationship exists between the physical-chemical properties of the amino acids and which of the A (U), U (A), or C (G) nucleotide is used in the second codon (anticodon) position. The amino acids coded for by G (C) in the second codon (anticodon) position do not participate in this relationship.  相似文献   

2.
    
Methods for robust comparison of bivariate errors-in-variables are considered. The concept of median lines is introduced for the robust estimation of principal components. Median lines separate the bivariate sample space into two equally sized parts. Statistical properties of the model parameters are derived. Robust residual analysis assesses linear relationships as well as goodness of fit and allows for the detection of potential outliers. Special emphasis is laid on graphical methods. A bivariate box-plot is proposed for exploratory data analysis. The median lines procedure is illustrated by a real example.  相似文献   

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