只有在积极的例子中学习性质
Mehrad Ansari1, Andrew D White1
1Department of Chemical Engineering, University of Rochester Rochester NY 14627 USA andrew.white@rochester.edu.
概括
这项研究引入了一种新的半监督深度学习方法,使用积极未标记的学习来发现. 这种方法有效地预测抗微生物的特性,只使用积极的例子,克服数据的限制.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 深度学习模型需要广泛的标记数据,包括负面示例,这对于性质来说很少.
- 高通量选方法难以有效地为基数据库生成负面示例.
研究的目的:
- 开发一个半监督的深度学习框架,以使用有限的积极例子来预测性质.
- 为了应对序分析中缺少负数据的挑战.
主要方法:
- 实施的积极未标记 (PU) 学习策略:调整基础分类器和可靠的负面识别.
- 构建了深度学习模型,从序列中推断出溶性,血解,SHP-2结合和非污染活性.
- 对传统的正负分类 (PN) 进行评估的预测性能.
主要成果:
- 通过PU学习方法,只使用阳性数据,实现了竞争性预测性表现.
- 在有限的负面例子的情况下,在场景中证明了PU学习的有效性.
- 成功预测了多种抗微生物的特性.
结论:
- 半监督PU学习是一个可行的替代品PN学习当负面数据稀缺时.
- 这种方法可以有效地预测性质,并指导分子设计.
- 在抗微生物研究中推进深度学习的应用.
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