为了在临床上可靠地使用变异效应的机器学习模型
Mafalda Dias1, Rose Orenbuch2, Debora S Marks3
1Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain; University Pompeu Fabra, Barcelona, Spain.
American journal of human genetics
|November 19, 2024
概括
深度学习模型预测蛋白质编码基因误解替代,有助于临床变异注释. 然而,目前的验证策略存在局限性,需要改进的方法来可靠地预测变异效应.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 分子遗传学 分子遗传学
背景情况:
- 深度学习模型对预测蛋白质编码基因中的误解替代具有前景.
- 准确的变体效应预测对于大规模的临床变体注释和患者测序至关重要.
- 对模型性能,适用性和稳定性的可靠评估对于临床实用性至关重要.
研究的目的:
- 解决用于验证in-silico变异效应预测模型的现有策略的局限性.
- 为这些模型的更可靠和更有影响的使用提出核心原则和建议.
- 加强在诊断和治疗中对变异效应预测的临床应用.
主要方法:
- 对ClinGen序列变异解释工作组提出的验证策略进行批判性评估.
- 制定改善模型验证和校准的核心原则和建议.
- 专注于提高临床使用的in-silico预测的可靠性和稳定性.
主要成果:
- 确定了当前用于验证in-silico变体效应预测模型的战略的重大局限性.
- 提出了一套核心原则和可行的建议,以克服这些局限性.
- 旨在促进变异效应预测工具的更可靠和更有影响的应用.
结论:
- 对于变异效应预测模型的现有验证策略需要大幅改进.
- 提出的原则和建议为更可靠的临床变异注释提供了一条道路.
- 增强验证将增加患者测序在指导医疗决策中的影响.
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