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Designing of an artificial neural network model to evaluate the association of three combined Y-specific microsatellite loci on the actual and predicted postthaw motility in crossbred bull semen

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Title Designing of an artificial neural network model to evaluate the association of three combined Y-specific microsatellite loci on the actual and predicted postthaw motility in crossbred bull semen
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Creator Rajib Deb , Umesh Singh , Thirvvothur Venkatesan Raja , Sushil Kumar , Shrikant Tyagi , Rafeeque R. Alyethodi , Rani Alex , Gyanendra Sengar , Sheetal Sharma
 
Subject Artificial neural network ,Crossbred bull ,Multiple regression analysis ,Postthaw motility Semen
 
Description Not Available
The freezing of bull semen significantly hamper the motility of sperm which reduces the
conception rate in dairy cattle. The prediction of postthaw motility (PTM) before freezing
will be useful to take the decision on discarding or freezing of the germplasm. The artificial
neural network (ANN) methodology found to be useful in prediction and classification
problems related to animal science, and hence, the present study was undertaken to
compare the efficiency of ANN in prediction of PTM on the basis of the number of ejaculates,
volume, and concentration of sperms. The combined effect of Y-specific microsatellite
alleles on the actual and predicted PTM was also studied. The results revealed that
the prediction accuracy of PTM based on the semen quality parameters was comparatively
lower because of higher variability in the data set. The ANN gave better prediction accuracy
(34.88%) than the multiple regression analysis models (32.04%). The root mean square
error was lower for ANN (8.4353) than that in the multiple regression analysis (8.6168).
The haplotype or combined effect of microsatellite alleles on actual and predicted PTM was
found to be highly significant (P < 0.01). On the basis of results, it was concluded that the
ANN methodology can be used for prediction of PTM in crossbred bulls.
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Date 2020-05-20T06:18:20Z
2020-05-20T06:18:20Z
2015-01-13
 
Type Research Paper
 
Identifier Not Available
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http://krishi.icar.gov.in/jspui/handle/123456789/35860
 
Language English
 
Relation Not Available;
 
Publisher Elsevier