对功能性基因嵌入的输入数据模式选择的评估.
Felix Brechtmann1,2, Thibault Bechtler1, Shubhankar Londhe1
1TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
NAR genomics and bioinformatics
|November 9, 2023
概括
功能性基因嵌入将基因功能整合到机器学习中. 文献和蛋白质相互作用数据产生了有用的嵌入,但由于对研究良好的基因的偏见,需要谨慎.
科学领域:
- 基因组学和生物信息学
- 机器学习在生物学中的应用
背景情况:
- 功能性基因嵌入代表机器学习的数值基因功能.
- 自主监督学习算法从各种数据类型 (如omics,网络和文献) 中创建这些嵌入式.
- 缺乏对嵌入式构建数据模式的比较研究,阻碍了最佳模型开发.
研究的目的:
- 从不同的数据模式中获得的功能性基因嵌入的基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因.
- 评估它们在预测疾病基因关联,癌症驱动因素和表型-基因联系方面的表现.
- 评估全基因组关联研究 (GWAS) 信号的实用性.
主要方法:
- 使用各种数据源 (omics,PPI网络,文献,蛋白质序列) 生成功能基因嵌入.
- 通过训练下游任务的现成预测器进行基准嵌入.
- 与专用最先进的预测器进行性能比较.
主要成果:
- 预先计算的嵌入与简单的预测器匹配或超越专业模型.
- 文献和基于低通量PPI的嵌入在预测精选基因列表方面表现出色.
- 这些嵌入显示了对研究良好的基因的偏见,并没有改善GWAS信号预测.
结论:
- 功能性基因嵌入对于基因学中的机器学习非常有用.
- 从文献和低通量PPI中嵌入的内容是有效的,但可能有偏见.
- 仔细考虑数据源偏差对于可靠的机器学习应用在遗传学中至关重要.
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