Protein-Protein Interaction Prediction via Graph Signal Processing

Colonnese, Stefania; Petti, Manuela; Farina, Lorenzo; Scarano, Gaetano; Cuomo, Francesca

doi:10.1109/access.2021.3119569

Cited by 11 publications

(9 citation statements)

References 32 publications

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“…where Mk is shown in (10). Notice that this update function not only adaptively updates based on the error, but also has a time-varying parameter M [k], which is different from the GLMS, the GNLMS, and the GLMP algorithms as they only adaptively update the error.…”

Section: Gnlmp Algorithm Derivationmentioning

confidence: 99%

“…Research in graph signal processing (GSP) has shown to be the solution to resolve the problem of processing irregular data by extending classical signal processing techniques such as Fourier transform and wavelet transform to graphs utilizing the spectral graph theory [1][2][3][4][5][6]. GSP-inspired ideas have a broad area of applications in various fields of study such as analysing brain signals [7], monitoring 5G Networks [8], modeling temperature data [9], making protien-protien interaction prediction [10], and modeling traffic events [11]. By defining graph convolution in neural networks using the Graph Fourier Transform (GFT), GSP has entered the field of deep learning; architectures such as the ChebNet and the graph convolutional network (GCN) are both deep learning architectures based on GSP backbones [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise

Yan,

Adel,

Kuruoglu

2022

Preprint

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In this paper, we introduce an adaptive graph normalized least mean pth power (GNLMP) algorithm for graph signal processing (GSP) that utilizes GSP techniques, including bandlimited filtering and node sampling, to estimate sampled graph signals under impulsive noise. Different from leastsquares-based algorithms, such as the adaptive GSP Least Mean Squares (GLMS) algorithm and the normalized GLMS (GNLMS) algorithm, the GNLMP algorithm has the ability to reconstruct a graph signal that is corrupted by non-Gaussian noise with heavy-tailed characteristics. Compared to the recently introduced adaptive GSP least mean pth power (GLMP) algorithm, the GNLMP algorithm reduces the number of iterations to converge to a steady graph signal. The convergence condition of the GNLMP algorithm is derived, and the ability of the GNLMP algorithm to process multidimensional time-varying graph signals with multiple features is demonstrated as well. Simulations show the performance of the GNLMP algorithm in estimating steady-state and time-varying graph signals is faster than GLMP and more robust in comparison to GLMS and GNLMS.

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Section: Gnlmp Algorithm Derivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise

Yan,

Adel,

Kuruoglu

2022

Preprint

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“…RepGSP rewards links between “repulsive nodes” (i.e., nodes belonging to different communities). U 71 – 73 III. Factorization-based methods: factorize the network adjacency matrix to reduce the high-dimensional nodes in the graph into a lower dimensional representation space by conserving the node neighborhood structures.…”

Section: Introductionmentioning

confidence: 99%

Assessment of community efforts to advance network-based prediction of protein–protein interactions

et al. 2023

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Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

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“…It constructed an unsigned variational graph autoencoder, which combined the PPI network with amino acid sequence information to learn the features of proteins to predict PPI. Inspired by the methods of graph signal processing, Colonnese et al [25] considered node features on PPI networks as signals, and developed a Markov model to accomplish PPIs prediction. Lv et al [26] constructed a GNN-PPI model based on graph isomorphism network (GIN) to predict the interactions between protein-protein pairs.…”

Section: Introductionmentioning

confidence: 99%

Long-distance dependency combined multi-hop graph neural networks for protein–protein interactions prediction

Zhang

Chen

et al. 2022

BMC Bioinformatics

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Background Protein–protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of methods based on deep learning have emerged. However, these methods do not take into account the long-distance dependency information between each two amino acids in sequence. In addition, most existing models based on graph neural networks only aggregate the first-order neighbors in protein–protein interaction (PPI) network. Although multi-order neighbor information can be aggregated by increasing the number of layers of neural network, it is easy to cause over-fitting. So, it is necessary to design a network that can capture long distance dependency information between amino acids in the sequence and can directly capture multi-order neighbor information in protein–protein interaction network. Results In this study, we propose a multi-hop neural network (LDMGNN) model combining long distance dependency information to predict the multi-label protein–protein interactions. In the LDMGNN model, we design the protein amino acid sequence encoding (PAASE) module with the multi-head self-attention Transformer block to extract the features of amino acid sequences by calculating the interdependence between every two amino acids. And expand the receptive field in space by constructing a two-hop protein–protein interaction (THPPI) network. We combine PPI network and THPPI network with amino acid sequence features respectively, then input them into two identical GIN blocks at the same time to obtain two embeddings. Next, the two embeddings are fused and input to the classifier for predict multi-label protein–protein interactions. Compared with other state-of-the-art methods, LDMGNN shows the best performance on both the SHS27K and SHS148k datasets. Ablation experiments show that the PAASE module and the construction of THPPI network are feasible and effective. Conclusions In general terms, our proposed LDMGNN model has achieved satisfactory results in the prediction of multi-label protein–protein interactions.

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Protein-Protein Interaction Prediction via Graph Signal Processing

Cited by 11 publications

References 32 publications

Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise

Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise

Assessment of community efforts to advance network-based prediction of protein–protein interactions

Long-distance dependency combined multi-hop graph neural networks for protein–protein interactions prediction

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