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一个微调深度学习框架为泛物种促进者与时间和频率空间的伪时间序列分析

Ruimeng Li, Qinke Peng, Haozhou Li

IEEE journal of biomedical and health informatics

|May 9, 2025

概括

本研究介绍了ProTriCNN,这是一种用于促进者识别的深度学习方法,以及TransPro,这是一个提高跨物种表现的框架. 这些工具提高了在各种生物体中识别基因调节元件的准确性.

科学领域:

基因组学就是基因组学.
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:

促进者鉴定对于理解基因调节至关重要.
特定物种促进者的异质性挑战了现有的识别模型.
像TATA-box和Pribnow box这样的关键促销元素对于功能至关重要.

研究的目的:

开发先进的计算方法,用于跨物种准确地识别促进体.
为了应对不同生物体中促进体异质性的挑战.
通过深度学习和转移学习来提高促进者识别模型的性能.

主要方法:

ProTriCNN:一个深度学习模型,将推动者视为伪时间序列,以捕捉元素异质性.
TransPro:一个基于ProTriCNN的微调框架,利用进化树进行跨物种对齐.
促进元素和物种进化关系的表示.

主要成果:

与最先进的方法相比,ProTriCNN实现了优越的性能,平均精度增加了2.1%和马修斯系数增强了20%.
与ProTriCNN相比,TransPro进一步提高了高达8%的精度和25%的马修斯系数.
在处理促进物异质性和跨物种识别方面表现出有效性.

结论:

ProTriCNN和TransPro在促进者识别准确性和稳定性方面取得了重大进展.
提出的方法有效地解决了特定物种的促进体变异.
这些工具为基因组研究和基因机制研究提供了宝贵的资源.

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