机器学习用于蛋白质功能预测
Yi-Heng Zhu1, Zi Liu2, Yu Ding1
1College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China.
Methods in molecular biology (Clifton, N.J.)
|July 29, 2025
概括
准确的蛋白质功能预测对于理解细胞过程和疾病机制至关重要. 本综述对包括深度学习在内的计算方法进行了分类,以加快功能注释,克服实验限制.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 了解蛋白质功能对于理解细胞过程和疾病机制至关重要.
- 实验性蛋白质功能注释是精确的,但耗时且昂贵.
- 需要高效的计算方法来准确预测蛋白质功能.
研究的目的:
- 审查和分类著名的计算蛋白质功能预测方法.
- 讨论这些预测方法的应用.
- 突出基因本体学 (GO) 术语在功能预测中的重要性.
主要方法:
- 将方法分为基于模板检测,基于统计机器学习,基于深度学习和组合方法的方法.
- 通过基因本体学 (GO) 术语定义的蛋白质功能的突出的计算预测器的审查.
主要成果:
- 对蛋白质功能预测的各种计算方法的识别和分类.
- 讨论各种预测模型的优势和应用.
结论:
- 计算方法为实验性蛋白质功能注释提供了有效的替代方案.
- 机器学习和深度学习的进步正在提高预测的准确性.
- 精确的蛋白质功能预测有助于疾病研究和药物设计.
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