Use of artificial neuronal nets in automation of analysis and genetic identification of electrophoretic spectra of gliadin from durum wheat]

Each wheat cultivar has a characteristic spectrum of gliadins. This makes it possible to use blocks of the components of reserve proteins as genetic markers when estimating seed quality. However, identification of the blocks that constitute the electrophoretic spectrum is a complicated task. For this purpose artificial neural network (ANN) technology is proposed. Based on experimental data, a teaching database and testing databases have been created. ANN was shown to be highly efficient (efficiency up to 100%) expert system for deciphering the electrophoretic spectra of gliadins of durum wheat cultivars.     

Diversity and the origin of the European population of <Emphasis Type="Italic">Triticum dicoccum</Emphasis> (Schrank) Schuebl. As revealed by chromosome analysis

Cluster analysis of the Triticum dicoccum chromosome passports by artificial neural networks and UPGMA divided the European T. dicoccum population into two groups, West European and Volga-Balkan. The West European T. dicoccum accessions displayed a predominance of the marker translocation T7A:5B (67% of the accessions), which was also found in a few accessions from other countries (Turkey, Iran, and northern Africa), and were similar in chromosome C-banding patterns. The Volga-Balkan T. dicoccum accessions differed in the C-banding patterns of some chromosomes from the West European accessions, thus probably originating from another founder population. It was assumed that the T. dicoccum accessions carrying the T7A:5B translocation had a common origin and that the wild T. dicoccum population of the Middle East (Syria and Lebanon) contributed to the origin of West European T. dicoccum.     

The use of a self-organizing feature map for the treatment of the results of RAPD and ISSR analyses in studies on the genomic polymorphism in the genus <Emphasis Type="Italic">Capsicum</Emphasis> L.

The results of studies based on multilocus molecular analyses, including random amplified polymorphic DNA (RAPD), inter-simple sequence repeat (ISSR), and amplified fragment length polymorphism (AFLP) analyses, are usually presented in the form of images (electrophoregrams, photographs, etc.). The interpretation of this information is complicated, labor-consuming, and subjective. Artificial neural networks (ANNs), which are ideal image processors, may be useful when solving such tasks. The possibility of using ANNs for the treatment of the results of RAPD and ISSR analyses has been studied. The RAPD and ISSR fragment spectra of the genus Capsicum L. (peppers) were used in this study. The results of clustering the accessions studied by means of the unweighted pair-group method with arithmetic averages (UPGMA), which is often used for phylogenetic constructions based on RAPD and ISSR data, serve as expert estimates. Fundamentally new methods of genetic polymorphism estimation using ANN technologies, namely, self-organizing feature maps (SOFMs) have been developed. The results show that the clusters obtained with the use of UPGMA and SOFM coincide by more than 90%; taking into account that ANNs can deal with high noise levels and incomplete or contradictory data, the approach proposed may prove to be efficient.Translated from Genetika, Vol. 41, No. 2, 2005, pp. 269–278.Original Russian Text Copyright © 2005 by Ruanet, Kochieva, Ryzhova.     

The Use of Artificial Neural Networks for Species Identification of Aegilops Based on Analysis of D Genomes

The possibilities of the use of artificial neural networks (ANNs) for identification of some polyploid species of genus Aegilopsbased on the idiograms of theirDgenomes were demonstrated.     

The use of artificial neural networks for Aegilops species identification based on analysis of D genomes

The possibilities of the use of artificial neural networks (ANNs) for identification of some polyploid species of genus Aegilops based on the idiograms of their D genomes were demonstrated.     

Kohonen network study of the results of RAPD and ISSR analyses of genomic polymorphism in the genus Capsicum L

The results of studies based on multilocus molecular analyses, including random amplified polymorphic DNA (RAPD), inter-simple sequence repeat (ISSR), and amplified fragment length polymorphism (AFLP) analyses, are usually presented in the form of images (electrophoregrams, photographs, etc.). The interpretation of this information is complicated, labor-consuming, and subjective. Artificial neural networks (ANNs), which are ideal "image processors," may be useful when solving such tasks. The possibility of using ANNs for the treatment of the results of RAPD and ISSR analyses has been studied. The RAPD and ISSR spectra have been studied in fragments of DNA of plants from the genus Capsicum L. (peppers). The results of clustering the accessions studied by means of the unweighted pair-group method with arithmetic averages (UPGMA), which is often used for phylogenetic constructions based on RAPD and ISSR data, serve as expert estimates. Fundamentally new methods of genetic polymorphism estimation using ANN technologies, namely, self-organizing feature maps (SOFMs) have been developed. The results show that the clusters obtained with the use of UPGMA and SOFM coincide by more than 90%; taking into account that ANNs can deal with high noise levels and incomplete or contradictory data, the approach proposed may prove to be efficient.     

The Use of Artificial Neural Networks for Automatic Analysis and Genetic Identification of Gliadin Electrophoretic Spectra in Durum Wheat

Each wheat cultivar has a characteristic spectrum of gliadins. This makes it possible to use blocks of the components of reserve proteins as genetic markers when estimating seed purity and identity. However, identification of the blocks that constitute the electrophoretic spectrum is a complicated task. For this purpose artificial neural network (ANN) technology is proposed. Using experimental data, a teaching database and testing databases have been created. ANN was shown to be highly efficient (efficiency up to 100%) expert system for deciphering the electrophoretic spectra of gliadins of durum wheat cultivars.