期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 19, 期 6, 页码 3154-3159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
类别
资金
- National Key R&D Program of China [2020YFA0908700]
- National Nature Science Foundation of China [U1713212, 62176164, 62072315, 62073225, 61836005, 61972263, 62006157]
- Natural Science Foundation of Guangdong ProvinceOutstanding Youth Program [2019B151502018]
- Natural Science Foundation of Guangdong Province [2019A1515011608]
- Science and Technology Development Program of Jilin Province [20170204061SF]
- Guangdong Pearl River Talent Recruitment Program [2019ZT08X603]
- Shenzhen Science and Technology Innovation Commission [R2020A045]
- Public Technology Platform of Shenzhen City [GGFW2018021118145859]
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。
推荐
暂无数据