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- W381031830 abstract "With the advent of new bio-technologies there is a significant increase in both the variety and amount of data that needs to be analysed in order to extract biological meaning. This is evident from the masses of molecular data being produced as a result of the numerous genome sequencing projects as well as from data now produced from DNA microarray/chip technologies. In this paper, we describe analysis of data obtain from recent advances in time-of-flight mass spectrometry which is a new method for rapid, high-resolution separation of protein mixtures. We employ a feedforward artificial neural network to train the resultant protein profiles processed from a number of varieties of wheat in order to classify them. The results of this study are extremely positive with results ranging from 87.5% to 100% accuracy, and we discuss them in context with a number of challenging problems for further studies." @default.
- W381031830 created "2016-06-24" @default.
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- W381031830 date "2001-01-01" @default.
- W381031830 modified "2023-09-23" @default.
- W381031830 title "Applying artificial neural networks to the classification of wheat varieties processed via MALDI-TOF mass spectrometry" @default.
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