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Marker selection for the detection of trisomy 21 using generalized matrix learning vector quantization

机译:使用广义矩阵学习矢量量化检测21三体的标志物选择

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In this work we explore the relevance of markers that are used for the early detection of fetal chromosomal abnormalities. For medical applications, it is important to optimize the number of used markers with respect to the number of necessary clinical examinations. We use the Generalized Matrix Learning Vector Quantization (GMLVQ) method to identify the most relevant markers from a set of 18 clinical examinations. We cross-validated our results using ten different training and test sets and we repeated our experiments using different parameters of GMLVQ. We identified the seven most relevant markers and we found that with these seven markers we obtain results that are comparable with the results that can be achieved with the full set of 18 markers. The results are in line with previous work that is found in the literature.
机译:在这项工作中,我们探索了用于早期检测胎儿染色体异常的标记物的相关性。对于医学应用,重要的是要根据必要的临床检查次数来优化所使用标记的数量。我们使用广义矩阵学习矢量量化(GMLVQ)方法从一组18种临床检查中识别出最相关的标记。我们使用十个不同的训练和测试集对结果进行交叉验证,并使用不同的GMLVQ参数重复了我们的实验。我们确定了七个最相关的标记,并且我们发现,使用这七个标记,我们获得的结果可与使用18个标记的完整集合所获得的结果相媲美。结果与文献中的先前工作相符。

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