为临床应用选择变体解释工具:背景很重要
Josu Aguirre1, Natàlia Padilla1, Selen Özkan1
1Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, P/Vall d'Hebron, 119-129, 08035 Barcelona, Spain.
International journal of molecular sciences
|July 29, 2023
概括
选择最好的计算工具来分类遗传变异是一个挑战. 一个新的基于成本的框架有助于为临床应用选择适当的致病性预测因子,考虑到被拒绝的低置信度预测.
科学领域:
- 基因组医学是基因组医学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 将遗传变异分类为良性或致病性对于基因组医学至关重要.
- 存在众多的致病性预测因子,使得临床应用的工具选择变得困难.
- 现有的工具在各种临床场景的准确性和适用性方面面临挑战.
研究的目的:
- 开发一个基于成本的框架来选择最佳的病原性预测因子.
- 为在临床环境中评估计算工具提供一个系统的方法.
- 为了应对选择最适合基因查和诊断的预测因子的挑战.
主要方法:
- 开发了一个基于成本的框架,以最小的参数编码临床情景.
- 作为排斥分类器的治疗病原性预测因素,包括低置信度预测排斥.
- 在各种误解变异案例中比较了不同数量的预测器的性能.
主要成果:
- 没有任何一种单一的致病性预测因子在所有临床情景中显示出普遍的最佳性.
- 包括预测拒绝在内显著改变了对分类器性能的看法.
- 基于成本的框架为预测因素选择提供了一个细微的方法.
结论:
- 病原性预测因子的最佳选择取决于上下文和特定于场景.
- 纳入拒绝策略可以提高病原性预测因子在临床实践中的实用性.
- 拟议的框架为基因组医学的知情决策提供了有价值的工具.
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