A hybrid deep learning framework for predicting the protein-protein interaction between virus and host

Abstract Background: Viral infection and diseases are caused by various viruses involved in the protein-protein interaction (PPI) between virus and host, which are a threat to human health. Studying the virus-host PPI is beneficial to apprehending the mechanism of viral infection and developing new treatment drugs. Although several computational methods for predicting the virus-host PPI have been proposed, most of them are supported by the machine learning algorithms, making the hidden high-level feature difficult to be extracted. Results: We proposed a novel hybrid deep learning framework combined with four CNN layers and LSTM to predict the virus-host PPI only using protein sequence information. CNN can extract the nonlinear position-related features of protein sequence, and LSTM can obtain the long-term relevant information. L1-regularized logistic regression is applied to eliminate the noise and redundant information. Our model achieved the best performance on the benchmark dataset and independent set compared with other existing methods. Conclusion: Our method, through the hybrid deep neural network, is useful for predicting virus-host PPI using protein sequence alone, and achieved the best prediction performance compared with other existing methods, which is promising on the virus-host PPI prediction

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RPITER: A Hierarchical Deep Learning Framework for ncRNA–Protein Interaction Prediction

International Journal of Molecular Sciences ◽

10.3390/ijms20051070 ◽

2019 ◽

Vol 20 (5) ◽

pp. 1070 ◽

Cited By ~ 12

Author(s):

Cheng Peng ◽

Siyu Han ◽

Hui Zhang ◽

Ying Li

Keyword(s):

Neural Network ◽

Deep Learning ◽

Protein Interaction ◽

Rna Binding ◽

Prediction Models ◽

Rna Binding Proteins ◽

Biological Research ◽

Sequence Information ◽

Learning Framework ◽

Coding Method

Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implicated in many complex human diseases. Mostly ncRNAs function by interacting with corresponding RNA-binding proteins. The research on ncRNA–protein interaction is the key to understanding the function of ncRNA. However, the biological experiment techniques for identifying RNA–protein interactions (RPIs) are currently still expensive and time-consuming. Due to the complex molecular mechanism of ncRNA–protein interaction and the lack of conservation for ncRNA, especially for long ncRNA (lncRNA), the prediction of ncRNA–protein interaction is still a challenge. Deep learning-based models have become the state-of-the-art in a range of biological sequence analysis problems due to their strong power of feature learning. In this study, we proposed a hierarchical deep learning framework RPITER to predict RNA–protein interaction. For sequence coding, we improved the conjoint triad feature (CTF) coding method by complementing more primary sequence information and adding sequence structure information. For model design, RPITER employed two basic neural network architectures of convolution neural network (CNN) and stacked auto-encoder (SAE). Comprehensive experiments were performed on five benchmark datasets from PDB and NPInter databases to analyze and compare the performances of different sequence coding methods and prediction models. We found that CNN and SAE deep learning architectures have powerful fitting abilities for the k-mer features of RNA and protein sequence. The improved CTF coding method showed performance gain compared with the original CTF method. Moreover, our designed RPITER performed well in predicting RNA–protein interaction (RPI) and could outperform most of the previous methods. On five widely used RPI datasets, RPI369, RPI488, RPI1807, RPI2241 and NPInter, RPITER obtained A U C of 0.821, 0.911, 0.990, 0.957 and 0.985, respectively. The proposed RPITER could be a complementary method for predicting RPI and constructing RPI network, which would help push forward the related biological research on ncRNAs and lncRNAs.

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Prediction of membrane protein types by fusing protein-protein interaction and protein sequence information

Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics ◽

10.1016/j.bbapap.2020.140524 ◽

2020 ◽

Vol 1868 (12) ◽

pp. 140524

Author(s):

Xiaolin Zhang ◽

Lei Chen

Keyword(s):

Membrane Protein ◽

Protein Interaction ◽

Protein Sequence ◽

Sequence Information ◽

Protein Protein Interaction

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Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

Electronics ◽

10.3390/electronics10101136 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1136

Author(s):

David Augusto Ribeiro ◽

Juan Casavílca Silva ◽

Renata Lopes Rosa ◽

Muhammad Saadi ◽

Shahid Mumtaz ◽

...

Keyword(s):

Deep Learning ◽

Image Quality ◽

Intelligent Transportation Systems ◽

Light Field ◽

State Of The Art ◽

Intelligent Transportation ◽

Transportation Systems ◽

Machine Learning Algorithms ◽

Learning Framework ◽

Reconstruction Methods

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

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WinBinVec: Cancer-Associated Protein-Protein Interaction Extraction and Identification of 20 Various Cancer Types and Metastasis Using Different Deep Learning Models

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2021.3093441 ◽

2021 ◽

pp. 1-1

Author(s):

Sina Abdollahi ◽

Peng-Chan Lin ◽

Jung-Hsien Chiang

Keyword(s):

Deep Learning ◽

Protein Interaction ◽

Learning Models ◽

Protein Protein Interaction ◽

Interaction Extraction ◽

Cancer Types

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Multimodal deep representation learning for protein interaction identification and protein family classification

BMC Bioinformatics ◽

10.1186/s12859-019-3084-y ◽

2019 ◽

Vol 20 (S16) ◽

Cited By ~ 4

Author(s):

Da Zhang ◽

Mansur Kabuka

Keyword(s):

Protein Interactions ◽

Protein Sequence ◽

Representation Learning ◽

Superior Performance ◽

Sequence Information ◽

Protein Protein Interactions ◽

Learning Framework ◽

Topological Features ◽

Ppi Networks ◽

Ppi Prediction

Abstract Background Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge. Results In this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods. Conclusion To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.

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