Abstracts

Information biology

The wide use of correlation analysis is due to possibility of real regularities evaluation and quantitative estimation of characters interrelation, but adequate content interpretation of results obtained is complicated by simultaneous effects of different variability factors being generally unknown. We use the original approach of factor bias when direct data unification is unreasonable. Multi-dimensional unbalanced randomized addition linear model with stochastic factors was taken as a pattern of character formation to arrange a more correct heterogeneous data unification for further correlation analysis. Unified set of unorganised samples is close to a randomised data matrix and that of stochastic factor values. Such a scheme of data organisation provides adequate results and it has a range of advantages of a full-scale experiment. So, the task of correct unification and further analysis becomes quite possible and is confined the formation of quasi-homogenous set of samples on every character by means of linear data transformations. Three examples of new approach application are connected with common wheat (Triticum aestivum) and sea-buckthorn (Hippophae rhamnoides) data. It was shown that the information output raised 3-6 times. We demonstrate that separate interrelations interfere each other in phenotype expression so could not be detected by direct analysis. The factor bias approach offered has several advantages:

1. The correlation complexes caused by separate variability factors can be evaluated.
2. Further adequate content analysis of corrected data can be carried out.
3. The information output maintains significantly.
4. The important reasons of correlation instability and obscurity can be overcomed.

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©2001, Siberian Branch of Russian Academy of Science, Novosibirsk
©2001, United Institute of Computer Science SB RAS, Novosibirsk
©2001, Institute of Computational Techologies SB RAS, Novosibirsk
©2001, A.P. Ershov Institute of Informatics Systems SB RAS, Novosibirsk
©2001, Institute of Mathematics SB RAS, Novosibirsk
©2001, Institute of Cytology and Genetics SB RAS, Novosibirsk
©2001, Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk
©2001, Novosibirsk State University
Last modified 06-Jul-2012 (11:45:21)