计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 285-291.doi: 10.11896/jsjkx.201100195
陈伟, 李杭, 李维华
CHEN Wei, LI Hang, LI Wei-hua
摘要: 核小体定位指DNA双螺旋相对于组蛋白的位置,并在DNA的转录阶段起着重要的调节作用。依靠生物实验的手段测得核小体定位会消耗大量的时间和资源,因此基于计算方法利用DNA序列进行核小体定位预测成为了一个重要的研究方向。针对核小体定位预测中单一模型和单一编码在DNA序列特征表示和学习方面的不足,文中提出了一种端到端的集成深度学习模型FuseENup,利用3种编码方式从多个维度表示DNA数据,利用不同的模型从不同维度提取数据中隐含的关键特征,构造了一种全新的DNA序列表征模型。在4种数据集上进行20倍交叉验证,相比当前针对核小体定位预测问题综合性能最优的模型CORENup,FuseENup的准确度(Accuracy)和精度(Precision)在HS数据集上提高了3%和9%,在DM数据集上提高了2%和6%,在E数据集上提高了1%和4%,相比其他的机器学习和深度学习基准模型,FuseENup具有更好的性能。实验结果表明,FuseENup能提高核小体定位的预测准确度,说明了该方法的有效性和科学性。
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