AudioGene:基于计算机的非综合征性听力障碍遗传因素预测

Kyle R. Taylor, Adam P. DeLuca, Corey W. Goodman, Bruce W. Tompkins, T. Scheetz, M. Hildebrand, P. Huygen, Richard J. H. Smith, T. Braun, T. Casavant
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引用次数: 2

摘要

AudioGene是一个由爱荷华大学开发的软件系统,用于分类和预测表明导致疾病或增加疾病风险因素的基因突变。我们集中在一个简明的例子-最可能的遗传原因的一种特殊形式的遗传性听力损失- ADNSHL。尽管在过去十年中,收集基因组数据的成本和吞吐量都有了显著提高,但收集和解释有关疾病诊断的临床信息仍然缓慢、昂贵且容易出错。AudioGene在迭代过程中使用机器学习技术来优先考虑可能的疾病遗传风险因素,然后通过分子(湿实验室)分析进行验证。在我们目前的实现中,AudioGene达到67%的第一选择准确率(相比之下,使用多数分类器达到23%)。当考虑前三个选择时,准确率提高到83%。这对于降低基因筛选的成本以及增加新基因发现的力量具有许多意义。虽然AudioGene专注于听力损失,但其设计和潜在机制可以推广到许多其他疾病,包括心脏病、癌症和精神疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AudioGene: Computer-based prediction of genetic factors involved in non-syndromic hearing impairment
AudioGene is a software system developed at the University of Iowa to classify and predict gene mutations that indicate causal or increased risk factors of disease. We focus on a concise example — the most likely genetic causes of a particular form of inherited hearing loss — ADNSHL. Whereas the cost and throughput involved in the collection of genomic data have advanced dramatically during the past decade, gathering and interpreting clinical information regarding disease diagnosis remains slow, costly and error-prone. AudioGene employs machine-learning techniques in an iterative procedure to prioritize probable genetic risk factors of disease, which are then verified with a molecular (wet lab) assay. In our current implementation AudioGene achieves 67% first-choice accuracy (versus 23% using a majority classifier). When the top three choices are considered, accuracy increases to 83%. This has numerous implications for reducing the cost of genetic screening as well as increasing the power of novel gene discovery efforts. While AudioGene is focused on hearing loss, the design and underlying mechanisms are generalizable to many other diseases including heart disease, cancer and mental illness.
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