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BPAGS:一种通过使用交替决策树,遗传算法和线性支向量分类器的特征评估进行细菌素预测的网络应用程序.

Suraiya Akhter^1,2, John H Miller²

¹School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States.

Frontiers in bioinformatics

|January 25, 2024

概括

这项研究开发了一个机器学习网络应用程序,BPAGS,以准确预测新型细菌素,这是对抗生素耐药性的有希望的策略. 最好的模型达到99.11%的准确性,超过现有方法.

关键词:

抗微生物类的抗微生物.抗微生物耐药性抗微生物耐药性细菌素预测的预测发现药物的发现.功能选择功能选择机器学习是机器学习.网络应用程序网络应用程序

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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
药物发现药物发现药物发现

背景情况:

抗生素耐药性需要新的治疗策略.
细菌素为传统抗生素提供了一个有希望的替代品.
对新型细菌素的准确预测对于药物开发至关重要.

研究的目的:

开发一个精确和高效的计算模型来预测新型细菌菌.
创建一个用户友好的网络应用程序,用于细菌素预测.
将机器学习方法与现有的预测工具进行比较.

主要方法:

从物理化学,结构和序列概况属性中提取特征.
使用交替决策树 (ADTree),遗传算法 (GA) 和线性支向量分类器 (线性SVC) 进行特征选择.
使用随机森林 (RF),支持向量机 (SVM),决策树 (DT),后勤回归 (LR),k-最近邻居 (KNN) 和高斯素朴湾 (GNB) 的模型构建.

主要成果:

使用ADTree降低功能的支持矢量机 (SVM) 模型实现了最高的精度 (99.11%) 和AUC (0.9984).
开发的Web应用程序BPAGS集成了基于ADTree,GA和线性SVC的预测模型.
对比分析显示,开发的模型的性能优于序列对齐和深度学习方法.

结论:

机器学习,特别是具有ADTree选择特征的SVM,为细菌素预测提供了高度准确的方法.
BPAGS网络应用程序为识别新型细菌素提供了一个有价值的工具.
这种方法有助于开发新药来对抗抗生素耐药性.