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首页> 外文会议>Annual International Conference on Research in Computational Molecular Biology >Ultra-Accurate Complex Disorder Prediction: Case Study of Neurodevelopmental Disorders
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Ultra-Accurate Complex Disorder Prediction: Case Study of Neurodevelopmental Disorders

机译：超准确的复杂性疾病预测：神经发育障碍的案例研究

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Motivation and Problem Definition. Early prediction of complex disorders (e.g. autism, intellectual disability or schizophrenia) is one of the main goals of personalized genomics and precision medicine. Considering the high genetic heritability of neurodevelopmental disorders (h~2 > 0.5 for autism [1]) we are proposing a novel problem and framework for accurate prediction of autism and related disorders based on rare and de novo genetic variants [2]. However, a positive diagnosis/prediction of a complex disorder (e.g., autism or intellectual disability) can have a severe negative psychological and economical impact on affected individuals and their family. Thus, one of the primary practical constraints in developing models and methods for prediction of a severe complex disorder is to guarantee a false positive prediction/discovery rate (FDR) of virtually zero. Hence, we are introducing a novel problem for prediction of complex disorders for a subset of affected cases with very low false positive prediction. We denote this problem as Ultra-Accurate Disorder Prediction (UADP) problem.
机译：动机和问题定义。复杂的病症（例如自闭症，智力障碍或精神分裂症）的早期预测是个性化的基因组学和精度医学的主要目标之一。考虑神经发育障碍的高遗传遗传（H〜2> 0.5孤独症[1]），我们提议基于稀有和从头遗传变异体[2]为孤独症的精确预测和相关病症的新的问题和框架。然而，复杂疾病的阳性诊断/预测（例如，自闭症或智力残疾）可以对受影响的个人和他们的家庭有严重的负面心理和经济的影响。因此，在开发的模型和方法为严重的复杂病症的预测的主要实际约束之一是保证几乎为零假阳性预测/发现率（FDR）。因此，我们引入了复杂的病症的预测的新的问题为受影响的情况下，一个子集具有非常低的假阳性预测。我们表示这个问题，因为超高精度障碍预测（UADP）的问题。

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