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NFEmbed:通过预训练的蛋白质嵌入物进行分类和回归来建模酶活性
Md Muhaiminul Islam Nafi1,2, Abdullah Al Mohaimin1
1Department of CSE, BUET, Dhaka 1000, Bangladesh.
机器学习模型预测微生物菌株的酶活性,为合成肥料提供可持续的替代品. 这些模型通过识别高效的固微生物来提高作物产量并减少环境影响.
科学领域:
- 农业科学 农业科学
- 生物信息学是一种生物信息学.
- 微生物学 微生物学
背景情况:
- 合成化肥会导致环境问题,如肥化和降低作物产量.
- 固定的微生物通过利用酶酶提供了一个可持续的替代方案.
- 在谷物作物中表达功能性酶是增强固化的潜在策略.
研究的目的:
- 使用机器学习预测具有高基酶活性的微生物菌株.
- 以基因组数据为基础,对潜在的固菌株进行查和排名.
- 开发用于生物肥料发现的先进计算工具.
主要方法:
- 探索蛋白质语言模型嵌入用于预测.
- 开发了两个堆叠组合模型:NFEmbed-C和NFEmbed-R.
- 利用了机器学习算法,包括k-Nearest Neighbors,随机森林,决策树回归器,极端梯度增强回归器和支持向量回归器.
主要成果:
- 两种NFEmbed-C和NFEmbed-R模型都超过了最先进的方法.
- NFEmbed-C获得了0.949的灵敏度,0.892的F1得分和0.784的马修斯相关系数.
- NFEmbed-R表现出强的表现,R2得分为0.783,MSE (0.158) 和RMSE (0.398) 低.
结论:
- 机器学习有效地预测微生物菌株中酶活性.
- 开发的模型为识别优质生物肥料候选人提供了强大的工具.
- 这种方法通过减少对合成肥的依赖来支持可持续农业.
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