一个多标签的学习框架,用于从生物合成基因集群预测天然产品的化学类和生物活动
1Software College, Shenyang Normal University, Shenyang, 110034, China. meisygle@gmail.com.
Journal of chemical ecology
|October 1, 2023
概括
这项研究引入了一个新的机器学习框架,可以直接从它们的生物合成基因集群中预测天然产品的功能. 这种方法可以在没有实验分析的情况下发现新的治疗化合物.
科学领域:
- 计算生物学 计算生物学
- 自然产品的发现自然产品的发现
- 机器学习 机器学习
背景情况:
- 自然产品对于药物发现和理解生态相互作用至关重要.
- 生物合成基因集群 (BGCs) 编码了负责NP合成的酶.
- 目前的方法通常需要广泛的实验NP结构解析.
研究的目的:
- 开发一个多标签的学习框架,用于BGCs的NP的功能注释.
- 在没有实验NP结构解析的情况下预测NP化学类和生物活性.
- 为了能够有效地发现新的治疗化合物.
主要方法:
- 设计了一个多标签的学习框架,使用BGC域作为特征和NP注释作为标签.
- 在一个低维空间中学习了特征和标签的联合表示.
- 该框架在实验数据上进行了评估,评估使用不平衡标签的性能.
主要成果:
- 拟议的框架实现了令人满意的多标签学习绩效.
- 学习了BGC域的模式,证明了它们在不同物种和王国 (例如,细菌到Arabidopsis thaliana) 之间具有可转移性.
- 一个管道整合BGC识别工具和框架被提出用于新型NP发现.
结论:
- 开发的框架准确地注释了来自生物合成基因集群的天然产品.
- 该模型能够跨物种转移知识,这有助于更广泛的NP发现.
- 这种方法支持对植物微生物群和土壤相互作用的下游生物生态研究.
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