通过知识驱动的深度神经网络模型从基因型数据中加强精神分裂症表型预测。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Daniel Martins , Maryam Abbasi , Conceição Egas , Joel P. Arrais
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引用次数: 0

摘要

本文探讨了深度学习模型的设计,从全能模型和遗传异质性概念中汲取灵感,利用基因型数据改进精神分裂症的预测。它介绍了一种创新的三步法,利用神经网络的能力来有效处理基因相互作用。首先,一个局部连接的网络将输入数据从变异株路由到其相应的基因。第二步采用编码器-解码器捕捉已识别基因之间的关系。最后的模型整合了前两步的知识,并加入了一个并行组件,以考虑更多基因的影响。这种扩展通过考虑更多的基因来提高预测得分。训练模型的平均 AUC 为 0.83,超过了其他基因型训练模型,并与基于基因表达数据集的方法相匹配。此外,对保留集的测试报告显示,平均灵敏度为 0.72,准确度为 0.76,与精神分裂症遗传性预测结果一致。此外,该研究通过考虑不同的人群子集,解决了遗传异质性的难题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models

Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models

This article explores deep learning model design, drawing inspiration from the omnigenic model and genetic heterogeneity concepts, to improve schizophrenia prediction using genotype data. It introduces an innovative three-step approach leveraging neural networks' capabilities to efficiently handle genetic interactions. A locally connected network initially routes input data from variants to their corresponding genes. The second step employs an Encoder-Decoder to capture relationships among identified genes. The final model integrates knowledge from the first two and incorporates a parallel component to consider the effects of additional genes. This expansion enhances prediction scores by considering a larger number of genes. Trained models achieved an average AUC of 0.83, surpassing other genotype-trained models and matching gene expression dataset-based approaches. Additionally, tests on held-out sets reported an average sensitivity of 0.72 and an accuracy of 0.76, aligning with schizophrenia heritability predictions. Moreover, the study addresses genetic heterogeneity challenges by considering diverse population subsets.

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CiteScore
7.20
自引率
4.30%
发文量
567
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