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An Overview of Reconstructability Analysis
- in Proceedings of 12th International World Organization of Systems and Cybernetics and 4th International Institute for General Systems Studies Workshop
, 2004
"... systems Reconstructability analysis (RA) is a method for detecting and analyzing the structure of multivariate categorical data. While Jones and his colleagues extended the original variable-based formulation of RA to encompass models defined in terms of system states, their focus was the analysis a ..."
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systems Reconstructability analysis (RA) is a method for detecting and analyzing the structure of multivariate categorical data. While Jones and his colleagues extended the original variable-based formulation of RA to encompass models defined in terms of system states, their focus was the analysis and approximation of real-valued functions. In this paper, we separate two ideas that Jones had merged together: the “g to k ” transformation and state-based modeling. We relate the idea of state-based modeling to established variable-based RA concepts and methods, including structure lattices, search strategies, metrics of model quality, and the statistical evaluation of model fit for analyses based on sample data. We also discuss the interpretation of state-based modeling results for both neutral and directed systems, and address the practical question of how state-based approaches can be used in conjunction with established variable-based methods. I.
Reconstructability Analysis With Fourier Transforms
- Kybernetes
, 2004
"... modeling Fourier methods used in 2- and 3-dimensional image reconstruction can be used also in reconstructability analysis (RA). These methods maximize a variance-type measure instead of information-theoretic uncertainty, but the two measures are roughly colinear and the Fourier approach yields resu ..."
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modeling Fourier methods used in 2- and 3-dimensional image reconstruction can be used also in reconstructability analysis (RA). These methods maximize a variance-type measure instead of information-theoretic uncertainty, but the two measures are roughly colinear and the Fourier approach yields results close to those of standard RA. The Fourier method, however, does not require iterative calculations for models with loops. Moreover the error in Fourier RA models can be assessed without actually generating the full probability distributions of the models; calculations scale with the size of the data rather than the state space. Statebased modeling using the Fourier approach is also readily implemented. Fourier methods may thus enhance the power of RA for data analysis and data mining. I.
Reconstructability Analysis of Epistasis 1
"... The final official.pdf of the article is available from the journal, or from PubMed, or, for researchers in non-profit institutions, from the author by request..Reconstructability Analysis of Epistasis 2 Summary The literature on epistasis describes various methods to detect epistatic interactions a ..."
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The final official.pdf of the article is available from the journal, or from PubMed, or, for researchers in non-profit institutions, from the author by request..Reconstructability Analysis of Epistasis 2 Summary The literature on epistasis describes various methods to detect epistatic interactions and to classify different types of epistasis. Reconstructability Analysis (RA) has recently been used to detect epistasis in genomic data (Shervais et al., 2010). This paper shows that RA offers a classification of types of epistasis at three levels of resolution (variable-based models without loops, variable-based models with loops, statebased models). These types can be defined by the simplest RA structures that model the data without information loss; a more detailed classification can be defined by the information content of multiple candidate structures. The RA classification can be augmented with structures from related graphical modeling approaches. RA can analyze epistatic interactions involving an arbitrary number of genes or SNPs and constitutes a flexible and effective methodology for genomic analysis.
