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SORFPP:增强丰富的序列驱动信息以识别基于验证数据集的融合框架的SEP

Hongqi Feng¹, Qi Nie¹, Sen Yang^1,2

¹School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, China.

|April 28, 2025

概括

一种新的计算方法,SORFPP,通过集成先进的蛋白质语言模型和传统特征,准确地预测短开放式读取框架编码 (SEPs). 这种高通量方法改进了用于识别这些关键调节分子的现有方法.

科学领域:

基因组学和分子生物学
生物信息学和计算生物学

背景情况:

在长非编码RNA (lncRNA) 中的短开放读取 (sORF) 编码具有重要调节作用的功能 (SEP).
现有的SEP预测计算方法缺乏足够的特征工程和有效的建模.
高通量计算方法对于准确有效的SEP识别至关重要.

研究的目的:

开发和验证一种用于预测SEP的新计算方法.
为解决目前的特征提取和SEP的预测建模的局限性.
使用综合方法提高SEP预测的准确性和稳定性.

主要方法:

开发了SORFPP,这是一个集成蛋白质语言模型ESM-2和传统编码 (QSOrder,k-mer) 的计算方法.
采用CatBoost来处理传统特征中的稀疏性,以及ESM-2特征的自我注意模型.
利用组合学习框架,结合多个模型,从逻辑回归模型进行最终预测.

主要成果:

与最先进的模型相比,SORFPP表现出优越的性能.
在三个基准数据集中,在马修相关系数中取得了显著的改进,从12.2%到24.2%不等.
验证了整合合体学习,传统特征和蛋白质语言编码的有效性.

结论:

拟议的SORFPP方法通过结合多种特征信息和集体学习,有效预测SEP.
将合并策略与传统和蛋白质语言编码方法相结合,可以提高预测性能.
该研究提供了可访问的数据集和代码,用于进一步研究SEP预测.

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