DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction
作者: Yanbu GuoWeihua LiBingyi WangHuiqing LiuDongming Zhou
刊名: BMC Bioinformatics, 2019, Vol.20 (1), pp.1-12
来源数据库: Directory of Open Access Journals
DOI: 10.1186/s12859-019-2940-0
关键词: Protein secondary structureDeep learningAsymmetric convolutional neural networkLong short-term memory
原始语种摘要: Abstract Background Protein secondary structure (PSS) is critical to further predict the tertiary structure, understand protein function and design drugs. However, experimental techniques of PSS are time consuming and expensive, and thus it’s very urgent to develop efficient computational approaches for predicting PSS based on sequence information alone. Moreover, the feature matrix of a protein contains two dimensions: the amino-acid residue dimension and the feature vector dimension. Existing deep learning based methods have achieved remarkable performances of PSS prediction, but the methods often utilize the features from the amino-acid dimension. Thus, there is still room to improve computational methods of PSS prediction. Results We propose a novel deep neural network method, called...
全文获取路径: DOAJ  (合作)
影响因子:3.024 (2012)

  • neural 神经系统的
  • convolutional 卷积
  • feature 结构元件
  • prediction 预报
  • memory 记忆
  • dimension 测定
  • secondary 二次的
  • protein 蛋白质
  • computational 计算的
  • short 短的