Mining topological structures of protein-protein interaction networks for human brain-specific genes

Wj, Cui; Xj, Gong; H, Yu; Zhang, XC

doi:10.4238/2015.october.16.10

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…In addition, they hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Hence, there is a strong motivation to develop efficient computational methods as alternative for inferring PPIs efficiently and accurately [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree

Zhou

Yu²,

Ding³

et al. 2017

PLoS ONE

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Nowadays a number of computational approaches have been developed to effectively and accurately predict protein interactions. However, most of these methods typically perform worse when other biological data sources (e.g., protein structure information, protein domains, or gene neighborhoods information) are not available. In the present work, we propose a method for predicting protein interactions making full use of physicochemical characteristics of amino acids. A protein sequence is encoded at multi-scale by seven properties, including their qualitative and quantitative descriptions, of amino acids. Five kinds of protein descriptors, frequency, composition, transformation, distribution and auto covariance, are extracted from these encodings for representing each protein sequence. The new formed feature representation consisted of 347 dimensions is able to capture not only the compositional and positional information but also their statistical significance of amino acids in the sequence. Based on such a feature representation, the gradient boosting decision tree algorithm is introduced to predict protein interaction class. When the proposed method is tested with the PPI data of S.cerevisiae, it achieves a prediction accuracy of 95.28% at the Matthew’s correlation coefficient of 90.68%. Compared with the state-of-the-art works on H.pylori and Human, the accuracies can be raised to 89.27% and 98.00% respectively. Extensive experiments are performed for a crossover protein-protein interactions network and the prediction accuracies are also very promising. Because of learning capabilities of the gradient boosting decision tree and the mutil-scale feature representation scheme, the proposed method might be a useful tool for future proteomics studies.

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Section: Introductionmentioning

confidence: 99%

Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree

Zhou

Yu²,

Ding³

et al. 2017

PLoS ONE

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show abstract

“…Proteins often act through functions with their partners. These interacting proteins regulate a variety of cellular functions, including cell-cycle progression, signal transduction, and metabolic pathways [ 1 ]. Therefore, the identification of protein–protein interactions (PPIs) can provide great insight into protein functions, further biological processes, drug target detection, and even treatment design [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences

Gong

et al. 2018

Molecules

115

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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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Strengthening Auto-Feature Engineering of Deep Learning Architecture in Protein–Protein Interaction Prediction

Mewara

Lalwani

2022

Communication and Intelligent Systems

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Mining topological structures of protein-protein interaction networks for human brain-specific genes

Cited by 3 publications

References 27 publications

Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree

Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree

Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences

Strengthening Auto-Feature Engineering of Deep Learning Architecture in Protein–Protein Interaction Prediction

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