4.7 Article

DeepCLD: An Efficient Sequence-Based Predictor of Intrinsically Disordered Proteins

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2021.3124273

关键词

Proteins; Feature extraction; Training; Amino acids; Prediction algorithms; Tools; Deep learning; Intrinsically Disordered Proteins; Deep Learning; ResNet; Attention Mechanism

资金

  1. National Key R&D Program of China [2020YFA0908700]
  2. National Nature Science Foundation of China [U1713212, 62176164, 62072315, 62073225, 61836005, 61972263, 62006157]
  3. Natural Science Foundation of Guangdong ProvinceOutstanding Youth Program [2019B151502018]
  4. Natural Science Foundation of Guangdong Province [2019A1515011608]
  5. Science and Technology Development Program of Jilin Province [20170204061SF]
  6. Guangdong Pearl River Talent Recruitment Program [2019ZT08X603]
  7. Shenzhen Science and Technology Innovation Commission [R2020A045]
  8. Public Technology Platform of Shenzhen City [GGFW2018021118145859]

向作者/读者索取更多资源

This paper proposes a novel algorithm, DeepCLD, for predicting intrinsically disordered proteins. The algorithm utilizes specific scoring matrix, ResNet, and bidirectional CudnnLSTM to improve prediction accuracy and efficiency.
Intrinsic disorder is common in proteins, plays important roles in protein functionality, and is commonly associated with various human diseases. To have an accurate tool for the annotation of intrinsic disorder in proteins, this paper proposes a novel algorithm, DeepCLD, for sequence-based prediction of intrinsically disordered proteins. This algorithm uses amino acid position specific scoring matrix (PSSM) to capture the intrinsic variability characteristic of sequence patterns, ResNet to preserve feature space structure, and bidirectional CudnnLSTM as recurrent layer to further improve the efficiency. Futhermore, DeepCLD also utilized the attention mechanism to solve the problem of gradient disappearing in deep network. Comparative analyses show that DeepCLD has faster training speed and higher prediction accuracy than comparable methods.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据