A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences

被引:6
|
作者
He, Jian [1 ]
Wu, Yanling [1 ]
Pu, Xuemei [1 ]
Li, Menglong [1 ]
Guo, Yanzhi [1 ]
机构
[1] Sichuan Univ, Coll Chem, Chengdu 610064, Peoples R China
基金
中国国家自然科学基金;
关键词
leukemia; protein phosphorylation site; protein primary sequences; machine-learning; deep-learning; transfer-learning; BACTERIAL; MODEL; LOGO;
D O I
10.3390/ijms23031741
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
As one of the most important post-translational modifications (PTMs), phosphorylation refers to the binding of a phosphate group with amino acid residues like Ser (S), Thr (T) and Tyr (Y) thus resulting in diverse functions at the molecular level. Abnormal phosphorylation has been proved to be closely related with human diseases. To our knowledge, no research has been reported describing specific disease-associated phosphorylation sites prediction which is of great significance for comprehensive understanding of disease mechanism. In this work, focusing on three types of leukemia, we aim to develop a reliable leukemia-related phosphorylation site prediction models by combing deep convolutional neural network (CNN) with transfer-learning. CNN could automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of leukemia-related phosphorylation site prediction. With the largest dataset of myelogenous leukemia, the optimal models for S/T/Y phosphorylation sites give the AUC values of 0.8784, 0.8328 and 0.7716 respectively. When transferred learning on the small size datasets, the models for T-cell and lymphoid leukemia also give the promising performance by common sharing the optimal parameters. Compared with other five machine-learning methods, our CNN models reveal the superior performance. Finally, the leukemia-related pathogenesis analysis and distribution analysis on phosphorylated proteins along with K-means clustering analysis and position-specific conversation profiles on the phosphorylation site all indicate the strong practical feasibility of our easy-to-use CNN models.
引用
收藏
页数:16
相关论文
共 50 条
  • [41] An efficient approach for guided wave structural monitoring of switch rails via deep convolutional neural network-based transfer learning
    Liu, Weixu
    Tang, Zhifeng
    Lv, Fuzai
    Chen, Xiangxian
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (02)
  • [42] Classification of age-related macular degeneration using convolutional-neural-network-based transfer learning
    Chen, Yao-Mei
    Huang, Wei-Tai
    Ho, Wen-Hsien
    Tsai, Jinn-Tsong
    BMC BIOINFORMATICS, 2021, 22 (SUPPL 5)
  • [43] Classification of age-related macular degeneration using convolutional-neural-network-based transfer learning
    Yao-Mei Chen
    Wei-Tai Huang
    Wen-Hsien Ho
    Jinn-Tsong Tsai
    BMC Bioinformatics, 22
  • [44] Comparison of Breast DCE-MRI Contrast Time Points for Predicting Response to Neoadjuvant Chemotherapy Using Deep Convolutional Neural Network Features with Transfer Learning
    Huynh, Benjamin Q.
    Antropova, Natasha
    Giger, Maryellen L.
    MEDICAL IMAGING 2017: COMPUTER-AIDED DIAGNOSIS, 2017, 10134
  • [45] DeepGly: A Deep Learning Framework With Recurrent and Convolutional Neural Networks to Identify Protein Glycation Sites From Imbalanced Data
    Chen, Jingui
    Yang, Runtao
    Zhang, Chengjin
    Zhang, Lina
    Zhang, Qian
    IEEE ACCESS, 2019, 7 : 142368 - 142378
  • [46] Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences
    Fan Cao
    Yu Zhang
    Yichao Cai
    Sambhavi Animesh
    Ying Zhang
    Semih Can Akincilar
    Yan Ping Loh
    Xinya Li
    Wee Joo Chng
    Vinay Tergaonkar
    Chee Keong Kwoh
    Melissa J. Fullwood
    Genome Biology, 22
  • [47] Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences
    Cao, Fan
    Zhang, Yu
    Cai, Yichao
    Animesh, Sambhavi
    Zhang, Ying
    Akincilar, Semih Can
    Loh, Yan Ping
    Li, Xinya
    Chng, Wee Joo
    Tergaonkar, Vinay
    Kwoh, Chee Keong
    Fullwood, Melissa J.
    GENOME BIOLOGY, 2021, 22 (01)
  • [48] Image-Based Incipient Fault Classification of Electrical Substation Equipment by Transfer Learning of Deep Convolutional Neural Network
    Guan, Xiangyu
    Gao, Wei
    Peng, Hui
    Shu, Naiqiu
    Gao, David Wenzhong
    IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING, 2022, 45 (01): : 1 - 8
  • [49] Classification of Damaged Grade on Rural Houses after Flood Disaster Based on Deep Convolutional Neural Network and Transfer Learning
    Wu L.
    Tong J.
    Wang Z.
    Ma D.
    Zhang J.
    Liao J.
    Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Science - Journal of China University of Geosciences, 2023, 48 (05): : 1742 - 1754
  • [50] Mispronunciation Detection Using Deep Convolutional Neural Network Features and Transfer Learning-Based Model for Arabic Phonemes
    Nazir, Faria
    Majeed, Muhammad Nadeem
    Ghazanfar, Mustansar Ali
    Maqsood, Muazzam
    IEEE ACCESS, 2019, 7 : 52589 - 52608