Prediction of Protein-Protein Interactions from Amino Acid Sequences Based on Continuous and Discrete Wavelet Transform Features

Wang, Tao; Li, Liping; Huang, Yuan; Zhang, Hui; Ma, Yahong; Xing, Zhou

doi:10.3390/molecules23040823

Cited by 22 publications

(15 citation statements)

References 44 publications

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“…The most recent work for predicting PPIs using sequences was presented by Wang et al [ 41 ]. They encoded protein sequences by combing the continuous and discrete wavelet transforms and used a weighted sparse-representation-based classifier for predicting.…”

Section: Discussionmentioning

confidence: 99%

Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences

Gong

et al. 2018

Molecules

107

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Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods makes the prediction task more tedious and trivial. The emerging deep learning technology enabling automatic feature engineering is gaining great success in various fields. However, the over-fitting and generalization of its models are not yet well investigated in most scenarios. Here, we present a deep neural network framework (DNN-PPI) for predicting PPIs using features learned automatically only from protein primary sequences. Within the framework, the sequences of two interacting proteins are sequentially fed into the encoding, embedding, convolution neural network (CNN), and long short-term memory (LSTM) neural network layers. Then, a concatenated vector of the two outputs from the previous layer is wired as the input of the fully connected neural network. Finally, the Adam optimizer is applied to learn the network weights in a back-propagation fashion. The different types of features, including semantic associations between amino acids, position-related sequence segments (motif), and their long- and short-term dependencies, are captured in the embedding, CNN and LSTM layers, respectively. When the model was trained on Pan’s human PPI dataset, it achieved a prediction accuracy of 98.78% at the Matthew’s correlation coefficient (MCC) of 97.57%. The prediction accuracies for six external datasets ranged from 92.80% to 97.89%, making them superior to those achieved with previous methods. When performed on Escherichia coli, Drosophila, and Caenorhabditis elegans datasets, DNN-PPI obtained prediction accuracies of 95.949%, 98.389%, and 98.669%, respectively. The performances in cross-species testing among the four species above coincided in their evolutionary distances. However, when testing Mus Musculus using the models from those species, they all obtained prediction accuracies of over 92.43%, which is difficult to achieve and worthy of note for further study. These results suggest that DNN-PPI has remarkable generalization and is a promising tool for identifying protein interactions.

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Section: Discussionmentioning

confidence: 99%

Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences

Gong

et al. 2018

Molecules

107

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“…Currently, many kinds of computational models based on protein sequences have been presented for predicting PPIs. In this section, to further objectively validate the prediction performance of the proposed method, seven state-of-the-art methods, including Ensemble Deep Neural Networks (EnsDNN) [22], 3-mers-based [31], Bio2vec-based [31], pseudo Substitution Matrix Representation (pseudo-SMR) [32], WSRC with continuous wavelet and discrete wavelet transform (WSRC+CW and DW) [33], feature weighted rotation forest algorithm (FWRF) [17], and Global encoding [34] were compared on the human, H. pylori, and yeast data sets. The comparison results of three benchmark data sets based on five-fold cross-validation of different models are plotted in Figures 2-4, respectively.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information

Xu¹,

Xu²,

Zhang³

et al. 2020

Molecules

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Identification of protein-protein interactions (PPIs) plays an essential role in the understanding of protein functions and cellular biological activities. However, the traditional experiment-based methods are time-consuming and laborious. Therefore, developing new reliable computational approaches has great practical significance for the identification of PPIs. In this paper, a novel prediction method is proposed for predicting PPIs using graph energy, named PPI-GE. Particularly, in the process of feature extraction, we designed two new feature extraction methods, the physicochemical graph energy based on the ionization equilibrium constant and isoelectric point and the contact graph energy based on the contact information of amino acids. The dipeptide composition method was used for order information of amino acids. After multi-information fusion, principal component analysis (PCA) was implemented for eliminating noise and a robust weighted sparse representation-based classification (WSRC) classifier was applied for sample classification. The prediction accuracies based on the five-fold cross-validation of the human, Helicobacter pylori (H. pylori), and yeast data sets were 99.49%, 97.15%, and 99.56%, respectively. In addition, in five independent data sets and two significant PPI networks, the comparative experimental results also demonstrate that PPI-GE obtained better performance than the compared methods.

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“…We can ignore the influence of inverse functions without differential coefficients at the extreme points or end points of when we calculate Eq. (10). Therefore, the value of , ∈ , and = 1, 2, ..., at the local area of these points is readjusted to ensure that exists, whereas the value of Φ remains unchanged.…”

Section: Fig 2 Function That Is Defined In Finite Monotonic Intervalsmentioning

confidence: 99%

“…Examples of commonly used signal decomposition methods in this category include various filter methods and empirical mode decomposition [5][6][7][8]. Commonly applied signal transformation methods include Fourier transform, wavelet transform [9,10] and Hilbert transform. In fault diagnosis, we first use these methods to obtain the original signal features.…”

Section: Introductionmentioning

confidence: 99%

Method for the extraction of shock signal features based on the upper limit of density integral

Yang¹,

Hong-xia²

2019

J. vibroeng.

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Shock signal features must be extracted for use in pattern recognition or fault diagnosis. In this work, we proposed a method for the feature extraction of shock signals, which are vibration signals that change faster and have larger amplitude ranges than general signals. First, we proposed the concepts of amplitude density for monotonic functions and piecewise monotonic functions. On the basis of these concepts, we then proposed the concept of the upper limit of density integral (ULDI), which was adopted to obtain signal features. Then, we introduced two types of serious fault cracks to the latch sheet of an automatic gun mechanism that is used on warships. Next, we applied the proposed method to extract the features of shock signals from data acquired when the automatic gun mechanism fired with normal and two fault patterns. Finally, we verified the effectiveness of our proposed method by applying the features that it extracted to a support vector machine (SVM). Our proposed method provided good results and was superior to the traditional statistics-based feature extraction method when applied to a SVM for classification. In addition, the former method demonstrated better generalisation than the latter. Thus, our method is an efficient approach for extracting shock signal features in pattern recognition and fault diagnosis.

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Prediction of Protein-Protein Interactions from Amino Acid Sequences Based on Continuous and Discrete Wavelet Transform Features

Cited by 22 publications

References 44 publications

Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences

Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences

Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information

Method for the extraction of shock signal features based on the upper limit of density integral

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