iAFP-fLRM:通过混合深度学习架构和多模式特征融合,精确识别抗真菌
Shengli Zhang1, Jianwei Cheng2, Guixu Zhou2
1School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China.
Biophysical chemistry
|February 7, 2026
概括
一个新的深度学习模型,iAFP-fLRM,从氨基酸序列准确预测抗真菌 (AFPs). 这种计算工具有助于发现新型抗真菌疗法,以对抗耐药性真菌感染.
科学领域:
- 计算生物学是一种计算生物学.
- 生物技术是生物技术.
- 药物发现 药物发现
背景情况:
- 真菌感染 (真菌菌) 是一个日益增长的全球健康威胁,特别是在免疫力低下的人群中.
- 抗真菌药物耐药性和有限的传统治疗疗效凸显出需要新的抗真菌策略.
- 抗真菌 (AFPs) 由于广泛的活性和低抗性潜力,显示出有希望的替代品,但实验性鉴定具有挑战性.
研究的目的:
- 开发一个计算框架,只使用氨基酸序列来准确和高效地预测抗真菌 (AFP).
- 解决实验性AFP识别的局限性,这是昂贵的,劳动密集型和耗时的.
主要方法:
- 开发了iAFP-fLRM,这是一个混合深度学习框架,集成了BLOSUM62的进化特征,令牌/位置嵌入,变压器编码器和LSTM-ResMLP分类器.
- 实施了双分支功能融合模块,具有适应性聚合对齐和跨分支关注,以增强功能互补性.
- 使用基准数据集用于模型培训和评估.
主要成果:
- 在精度,AUC和MCC方面,iAFP-fLRM在最先进的方法中表现优越.
- 除研究证实了手工制作和学习特征结合的有效性.
- t-SNE可视化显示该模型能够有效地区分AFP和非AFP.
结论:
- iAFP-fLRM提供了一个强大的和可扩展的计算工具,用于在中识别抗真菌.
- 这种工具可以加速发现新型抗真菌和开发新的抗真菌疗法.
- 该研究为进一步研究提供了可访问的代码和数据集.
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