PMTPred:基于机器学习的蛋白甲基转移酶的预测,使用k间隔的氨基酸对的组成
Arvind Kumar Yadav1, Pradeep Kumar Gupta2,3, Tiratha Raj Singh4,5
1Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Solan- 173234, Himachal Pradesh, India.
Molecular diversity
|July 20, 2024
概括
我们开发了PMTPred,这是一种机器学习工具,可以准确识别蛋白甲基转移酶 (PMTs),这是表观遗传调节和癌症中的关键酶. 这种基于SVM的方法为研究提供了可靠的PMT识别.
科学领域:
- 生物化学 生物化学
- 生物信息学是一种生物信息学.
- 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
背景情况:
- 蛋白甲基转移酶 (PMT) 是对表观遗传调节至关重要的酶,催化甲基组转移到DNA,RNA和蛋白质等多种基质.
- 对PMTs的调节失调与各种人类癌症有关,这突显了需要有效的识别方法的需要.
研究的目的:
- 开发一种可靠和高效的基于机器学习的方法来识别蛋白甲基转移酶 (PMT).
主要方法:
- 基于序列的特征被计算并用于训练各种机器学习算法的预测模型.
- 使用十倍交叉验证技术来评估模型性能.
- 使用具有CKSAAP特征的支持矢量机 (SVM) 算法,以及用于特征重要性评估的SHAP值.
主要成果:
- 基于SVM的CKSAAP模型显示出最高的预测准确度,实现了平衡的灵敏度和特异性.
- 这种SVM模型在PMT预测中表现优于深度学习算法.
- 交叉数据库验证证实了该模型的稳定性,导致其作为PMTPred工具的实施.
结论:
- PMTPred是识别蛋白甲基转移酶的强大而有效的工具,有助于表观遗传学和癌症研究.
- 开发的基于SVM的CKSAAP模型为PMT识别提供了一种可靠的方法,其性能优于现有的深度学习方法.
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