A priori determination of the sex of a human individual before gestation is a desirable goal in some cases. To achieve this, it is necessary to perform the separation of sperm cells containing either X or Y chromosomes. As is well known, male sex depends on the presence of chromosome Y. Once this separation is achieved in principle, we require to determine, with a high degree of accuracy, whether the sperm cells of interest contain the desired X or Y chromosomes. If we are able to obtain certain simple measurements regarding the sperm cells under consideration we will be able to control the fertilization process reliably. In this paper we report a method which allows for non-invasive verification of the characteristics of the separated sperm. We determined a set of easily measurable characteristics. From a sample drawn from previously cropped sperm we trained a neural network with a genetic algorithm. The trained network was able to perform a posteriori classification with an error much smaller than 1%. This percentage of efficiency is better than the ones reported in centers of assisted fecundation.

Kuri, A., Ortiz, M., Zenteno, D., Penaloza, R. (2003). Classification of Sperm Cells According to their Chromosomic Content Using a Neural Network Trained with a Genetic Algorithm. In A new beginning for human health : proceedings of the 25th annual international conference of the IEEE Engineering in Medicine and Biology Society (pp.2253-2256). IEEE-Press [10.1109/IEMBS.2003.1280246].

Classification of Sperm Cells According to their Chromosomic Content Using a Neural Network Trained with a Genetic Algorithm

Penaloza, R
2003

Abstract

A priori determination of the sex of a human individual before gestation is a desirable goal in some cases. To achieve this, it is necessary to perform the separation of sperm cells containing either X or Y chromosomes. As is well known, male sex depends on the presence of chromosome Y. Once this separation is achieved in principle, we require to determine, with a high degree of accuracy, whether the sperm cells of interest contain the desired X or Y chromosomes. If we are able to obtain certain simple measurements regarding the sperm cells under consideration we will be able to control the fertilization process reliably. In this paper we report a method which allows for non-invasive verification of the characteristics of the separated sperm. We determined a set of easily measurable characteristics. From a sample drawn from previously cropped sperm we trained a neural network with a genetic algorithm. The trained network was able to perform a posteriori classification with an error much smaller than 1%. This percentage of efficiency is better than the ones reported in centers of assisted fecundation.
Si
paper
genetic algorithms, neural networks, classification
English
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
0-7803-7789-3
Kuri, A., Ortiz, M., Zenteno, D., Penaloza, R. (2003). Classification of Sperm Cells According to their Chromosomic Content Using a Neural Network Trained with a Genetic Algorithm. In A new beginning for human health : proceedings of the 25th annual international conference of the IEEE Engineering in Medicine and Biology Society (pp.2253-2256). IEEE-Press [10.1109/IEMBS.2003.1280246].
Kuri, A; Ortiz, M; Zenteno, D; Penaloza, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/233776
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