The protein-protein interaction network alignment using recurrent neural network

Mahdipour, Elham; Ghasemzadeh, Mohammad

doi:10.1007/s11517-021-02428-5

Cited by 4 publications

(3 citation statements)

References 49 publications

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“…In addition, information redundancy slows down the calculation time for the construction and visualization of networks ( Chen et al, 2019 , 2019 ). To limit and remove redundancy, different information scores have been set up ( Silverbush and Sharan, 2019 ; Mahdipour and Ghasemzadeh, 2021 ). The Mi-score ( Villaveces et al, 2015b ) consisting of three scores, is increasingly used to validate a PPI.…”

Section: Discussionmentioning

confidence: 99%

“…Within supervised methods, a sub-class of methods has emerged in recent years: self-supervised learning methods ( Chen et al, 2022 ; Murphy, Jegelka and Fraenkel, 2022 ), able to train themselves to learn and predict the output of one part of the input data from another part of the data ( Wang et al, 2021 ; Guo et al, 2022 ). A graph neural network is a self-supervised method for predicting interactions and in particular PPIs ( Mahdipour and Ghasemzadeh, 2021 ; Jha, Saha and Singh, 2022 ; Y. Wu et al, 2022b ).…”

Section: Methods Based On the Machine Learning Algorithmmentioning

confidence: 99%

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Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

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At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

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Section: Discussionmentioning

confidence: 99%

Section: Methods Based On the Machine Learning Algorithmmentioning

confidence: 99%

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

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show abstract

“…For PPI network prediction, Mahdipour et al [ 84 ] introduced RENA, an innovative method for PPI network alignment based on recurrent neural networks. Ortiz-Vilchis et al [ 85 ] employed a bidirectional LSTM model for generating relevant protein sequences with partial knowledge of interactions, demonstrating an ability to retain a significant portion of proteins in the original sequence.…”

Section: Recurrent Neural Network For Protein–protein Interactionsmentioning

confidence: 99%

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Lee

2023

Molecules

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Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein–Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.

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Network-Based Analysis for Biological Knowledge Discovery

Tripathi,

Allendes Osorio,

Murakami

et al. 2024

Reference Module in Life Sciences

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The protein-protein interaction network alignment using recurrent neural network

Cited by 4 publications

References 49 publications

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Network-Based Analysis for Biological Knowledge Discovery

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