Genetic analyses principal component analysis

Kohonen network Conceptual clustering Principal Component Analysis (PCA) Decision trees Partial Least Squares (PLS) Multiple Linear Regression (MLR) Counter-propagation networks Back-propagation networks Genetic algorithms (GA)... 

Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E., Shadick, N.A., and Reich, D. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904-909. Available at http //genepath.med.Harvard.edu/ reich/ EIGENSTRAT.htm. 

Xue, L. and Bajorath, J. (2000) Molecular descriptors for effective classification of biologically active compounds based on principal component analysis identified by a genetic algorithm. J. Chem. Inf. Comput. Sci. 40, 801-809. 

In Chapter 16, Lavine colleagues return to a compound classification problem by using a combination of principal component analysis and a genetic algo-... 

Thus, multilinear models were introduced, and then a wide series of tools, such as nonlinear models, including artificial neural networks, fuzzy logic, Bayesian models, and expert systems. A number of reviews deal with the different techniques [4-6]. Mathematical techniques have also been used to keep into account the high number (up to several thousands) of chemical descriptors and fragments that can be used for modeling purposes, with the problem of increase in noise and lack of statistical robustness. Also in this case, linear and nonlinear methods have been used, such as principal component analysis (PCA) and genetic algorithms (GA) [6]. 

Canonical Correlation Analysis Principal Component Regressionb Classification and Regression Trees (CART) Linear Learning Machine Neural Networks Adaptive Least Squares Genetic Programming Logistic Regression... 

Fig. 6-15 Principal component analysis of multidimensional, chemical-genetic data, (a) Eigenvalues and associated variance, and eigenvectors and associated factor scores computed from the data in Fig. 6-14(a). The matrix of eigenvectors...

Principal component analysis A statistical technique to summarize the observed high-dimensional data in a lower dimensional space through the linear combination of observed variables. It is commonly used to summarize genetic background information across study subjects. 

Computer methods for the reduction of measurement spaces, such as fast Fourier and fast Hadamard transforms, principal component analysis, autocorrelation, genetic algorithm, and others, can be applied successfully. 

The genetic code, regardless of whether it is a product of a "frozen accident" [1] or a deterministic interaction between the nucleotides and the amino acids [2], displays an apparent correlation between the nucleotides found at particular codon positions and the physico-chemical properties of the protein amino acid residues encoded by the nucleotides [2-10]. A variety of analytical methods have been employed to quantitatively examine these relationships. SjdstrOm and Wold [5], for example, have used Principal Component Analysis (PCA) to relate twenty physical properties of the amino acids to the genetic code. They find that 58% of the variance in the data can be accounted for by considering just three factors. In order of importance, the predominant contributions are 1) hydrophobicity, 2) molar volume, and 3) electronic descriptors (e.g. pKgS and NMR chemical shifts). Fig. 1 presents a concise display of... 

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