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Lampach, Stanley; Tovar, António; Taylor, Douglas R.; Porte, J.; Kravtchenko, Suzanne; Prieto, L. J.; Corréard, Geneviève; Fowkes, Robert A.; Brunner, Ingrid; Taylor, Douglas; Rudnyćkyj, J. B.; Manessy, G.

doi:10.1080/00437956.1957.11659642

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“…in the study of cold shock in yeast [13]. The powerful feature of PCA, detection of outliers, was adopted for the study of mutation [14]. Global approaches to quantitative analysis of gel patterns can be found in other articles [15-191. No classifications of 2-D profiles using neural networks has been reported so far.…”

Section: Introductionmentioning

confidence: 99%

Adaptive classification of two‐dimensional gel electrophoretic spot patterns by neural networks and cluster analysis

Vohradský

1997

Electrophoresis

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The interpretation of two-dimensional gel electrophoresis spot profiles can be facilitated by statistical and machine learning programs. Two different approaches to classification of spot profiles - cluster analysis and neural networks - are discussed. Neural networks for two different model patterns were designed and an algorithm for training of the net for the classification was developed. It was shown that the performance of neural networks is higher compared to cluster and principal component analysis. The possibility of combining both approaches into one process can increase reliability and speed of classification. Artificially created training sets with added random noise can be used for network training. The analysis was applied on the Streptomyces coelicolor developmental two-dimensional (2-D) gel database.

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Section: Introductionmentioning

confidence: 99%