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首页> 外文会议>International Conference on Bioinformatics Models, Methods and Algorithms >HAPLOTYPE-BASED CLASSIFIERS TO PREDICT INDIVIDUAL SUSCEPTIBILITY TO COMPLEX DISEASES: An Example for Multiple Sclerosis
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HAPLOTYPE-BASED CLASSIFIERS TO PREDICT INDIVIDUAL SUSCEPTIBILITY TO COMPLEX DISEASES: An Example for Multiple Sclerosis

机译：基于单倍型的分类剂，以预测对复杂疾病的单个易感性：多发性硬化的一个例子

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The enormous amount of genetic data that is currently being produced with the explosion of genome-wide association studies is yielding an important effort in the construction of genetic-based predictive models for individual susceptibility to complex diseases. However, a constant pattern of low accuracy is observed in most of them. We hypothesize that a main cause of their low accuracy is the strong reduction of genetic information considered by the classifiers, and propose a three-fold solution that considers haplotype instead of genotype individual data, whole-genome markers instead of a more stringent selection and several-marker risk variants instead of only one or two. We have compared the performance of our approach with current approaches to predict individual genetic risk to multiple sclerosis, and have found that our method yielded significantly more accurate classifiers.
机译：目前正在爆炸于基因组关联研究的巨大遗传数据，在构建基于遗传的预测模型的遗传性预测模型中，对复杂疾病的遗传学模型产生了重要努力。然而，在大多数情况下观察到恒定的低精度模式。我们假设其低精度的主要原因是分类器所考虑的遗传信息的强烈降低，并提出了一种三倍的解决方案，其考虑单倍型而不是基因型单个数据，全基因组标记而不是更严格的选择和几个 - 市场风险变体而不是一个或两个。我们已经将我们的方法的性能与目前的方法进行了比较，以预测多发性硬化的个体遗传风险，并发现我们的方法产生了更准确的分类器。

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