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在小规模和异质生物数据集中的域名适应

Seyedmehdi Orouji¹, Martin C Liu^2,3, Tal Korem^3,4,5

¹Department of Cognitive Sciences, University of California Irvine, Irvine, CA, USA.

Science advances

|December 20, 2024

概括

域调整,一种转移学习技术,有助于机器学习模型在各种生物数据集中进行概括. 这篇评论探讨了它的应用,好处和计算生物学家面临的挑战.

科学领域:

计算生物学是一种计算生物学.
机器学习是机器学习.
基因组学就是基因组学.

背景情况:

机器学习模型在现代生物学中至关重要,但由于技术和生物学变异,在不同的数据集中难以泛化.
现有的域适应方法,通常是为像图像这样的大规模数据设计的,对于生物数据的复杂,异质和功能丰富的性质没有优化.

研究的目的:

对专门针对生物数据集的领域适应方法进行审查.
让生物学家了解领域适应的好处和挑战.
为未来的研究指导,为生物学开发量身定制的领域适应技术.

主要方法:

在生物数据特征的背景下,审查当前的领域适应技术.
批判性地探索现有方法的目标,优点和弱点.
讨论小,高维和异质生物数据所带来的挑战.

主要成果:

域调整提供了一个解决方案,以改善跨生物队列和实验室的模型通用性.
目前的方法需要适应,以有效地处理生物数据的独特特性.
对于生物应用来说,需要定制的域适应方法.

结论:

域调整对于计算生物学家来说是一个有价值的技术,可以提高模型性能.
进一步开发专门的领域适应方法对于推动生物研究至关重要.
强烈建议将域调整整合到计算生物学家的工具包中.

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