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
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Updated: May 9, 2025
10:36
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
Published on: August 3, 2018
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Hongqi Feng1, Qi Nie1, Sen Yang1,2
1School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, China.
PloS one
|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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