The complexity of linkage analysis with neural networks
The complexity of linkage analysis with neural networks
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DOI：
10.1159/000053338
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发表时间：
2001-01-01
期刊：
影响因子：
1.8
通讯作者：
Weeks, DE
中科院分区：
文献类型：
--
作者：
Marinov, M;Weeks, DE
随着全基因组疾病基因座扫描的重点已经从简单的孟德尔性状转移到遗传复杂的特征，研究人员已经开始考虑新的替代方法来检测连锁，这种方法将不仅仅考虑单个疾病基因座的边际影响。一个有趣的新方法是在全基因组数据集上训练神经网络，以便在受影响的兄弟姐妹之间寻找受影响的兄弟姐妹在标记上分享的血统身份与他们的疾病状态之间的最佳非线性关系。我们在这里研究了神经网络结果的可重复性，并表明通过多次运行神经网络方法获得的结果可能会有很大的不同，这最可能是因为训练神经网络涉及最小化具有大量局部最小值的误差函数的事实。版权所有(C)2001 S.Karger AG，巴塞尔。
As the focus of genome-wide scans for disease loci have shifted from simple Mendelian traits to genetically complex traits, researchers have begun to consider new alternative ways to detect linkage that will consider more than the marginal effects of a single disease locus at a time, One interesting new method is to train a neural network on a genome-wide data set in order to search for the best non-linear relationship between identity-by-descent sharing among affected siblings at markers and their disease status. We investigate here the repeatability of the neural network results from run to run, and show that the results obtained by multiple runs of the neural network method may differ quite a bit, This is most likely due to the fact that training a neural network involves minimizing an error function with a multitude of local minima. Copyright (C) 2001 S. Karger AG, Basel.