中小企业-MFP:一种新的时空神经网络,具有多角度初始化嵌入,用于对多功能的预测
Jing Xu1, Xiaoli Ruan1, Jing Yang1
1State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China.
Computational biology and chemistry
|February 27, 2024
概括
这项研究介绍了SME-MFP,这是一个新的机器学习工具,用于预测功能,这是对抗生素有希望的替代品. 该方法通过有效捕获序列特征来提高准确性,特别是在不平衡的数据集中.
科学领域:
- 生物医学科学 生物医学科学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 功能性是一种低毒性,高吸收的替代传统抗生素.
- 机器学习有助于功能预测,但在多功能识别和不平衡数据方面存在困难.
研究的目的:
- 开发SME-MFP,用于不平衡的多标签功能数据集的新型预测器.
- 为了提高功能性标识的精度和特征提取能力.
主要方法:
- 利用物理化学和进化信息进行序列表示.
- 使用与空间 (剩余连接,多尺度CNN) 和时间 (AFT) 特征提取器的融合特征.
- 开发了一个新的损失函数,以解决多标签数据集中的类不平衡问题.
主要成果:
- 中小企业-多元金融机构框架有效地从多个角度捕捉了序列特征.
- 在公开的类数据集上,与现有方法相比,获得了3.89%的准确性改进.
- 证明了在识别功能性的增强模型性能,特别是在不平衡的场景中.
结论:
- 中小企业-MFP显著提高捕获序特征的能力.
- 拟议的预测器为功能鉴定提供了更高的准确性,特别是在挑战不平衡数据集时.
- 这项工作促进了机器学习应用在基于的治疗中.
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