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An Extensive Assessment of Network Embedding in PPI Network Alignment
2022
Entropy
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network alignment and tested on PPI networks; (v) two algorithms out of three are stated to perform multi-network alignment, while the remaining perform pairwise network alignment. ...
In this survey, we present an overview of current PPI network embedding alignment methods, a comparison among them, and a comparison to classical PPI network alignment algorithms. ...
The main focus of the present paper is to review existing representational learning approaches for the problem of aligning pairwise or multiple PPI networks. ...
doi:10.3390/e24050730
pmid:35626613
pmcid:PMC9141406
fatcat:zpiu4mmmvza5rggc2xksajdscm
Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review
2023
Molecules
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Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. ...
This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. ...
A few studies have concentrated on predicting PPIs based on coevolution signals from joint multiple sequence alignments. For instance, Pei et al. ...
doi:10.3390/molecules28135169
pmid:37446831
pmcid:PMC10343845
fatcat:anyokolzlveanakt5kyv4cbksa
NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity
[article]
2020
bioRxiv
pre-print
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In this work, we integrate sequence and network information across multiple species by applying an IsoRank-derived network alignment algorithm to create a meta-network profile of the proteins of multiple ...
We then use this integrated multispecies meta-network as input features to train a maxout neural network with Gene Ontology terms as target labels. ...
Most network alignment methods focus on this latter goal [29, 30, 31, 32, 33, 34, 35] . IsoRank [29] is a global network alignment algorithm used to align multiple PPI networks. ...
doi:10.1101/2020.07.30.227611
fatcat:hlson6rstfcnfdvu2v4zz3meii
NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity
2021
Bioinformatics
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In this work, we integrate sequence and network information across multiple species by computing IsoRank similarity scores to create a meta-network profile of the proteins of multiple species. ...
We use this integrated multispecies meta-network as input to train a maxout neural network with Gene Ontology terms as target labels. ...
Acknowledgements The authors thank Nicholas Carriero and Ian Fisk of the Flatiron Insitute for discussion and help with high performance computing. ...
doi:10.1093/bioinformatics/btab098
pmid:33576802
fatcat:zeqszhghvzd3nfxjm2nthxktsi
Learning Heat Diffusion for Network Alignment
[article]
2020
arXiv
pre-print
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Comparing EDNA with state-of-the-art algorithms on a popular protein-protein interaction network dataset, using four different evaluation metrics, we achieve (i) the most accurate alignments, (ii) increased ...
Yet, network alignment remains a core algorithmic problem. Here, we present a novel learning algorithm called evolutionary heat diffusion-based network alignment (EDNA) to address this challenge. ...
performance. calculated from extracted PPI network representations or other network alignment techniques, and on this basis we select nodes with highly confident alignments as anchor node-pairs. ...
arXiv:2007.05401v1
fatcat:psh4n53chnb3lmast6jegikbsa
Bio-JOIE
2020
Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
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2021 pre-print arXiv:2103.04283v1 fatcat:ok3plndb3rfwbn2m4fmwjrqip4
Furthermore, we also demonstrate the potential of leveraging the learned representations on clustering proteins with enzymatic function into enzyme commission families. ...
By leveraging only structured knowledge, Bio-JOIE significantly outperforms existing state-of-the-art methods in PPI type prediction on multiple species. ...
We compare Bio-JOIE with that of the state-of-the-art representation learning approaches on multiple species, including SARS-CoV-2-Human PPIs, with different model settings. ...
doi:10.1145/3388440.3412477
dblp:conf/bcb/HaoJCSZ020
fatcat:jqvjndnnjzf25mukvij4ozj4vq
Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases
[article]
2020
bioRxiv
pre-print
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Furthermore, we also demonstrate the potential of leveraging the learned representations on clustering proteins with enzymatic function into enzyme commission families. ...
By leveraging only structured knowledge, Bio-JOIE significantly outperforms existing state-of-the-art methods in PPI type prediction on multiple species. ...
We compare Bio-JOIE with that of the state-of-the-art representation learning approaches on multiple species, including SARS-CoV-2-Human PPIs, with different model settings. ...
doi:10.1101/2020.06.15.153692
fatcat:lh4cm424cffcxnzgkxs3y5sbvy
Network Alignment with Holistic Embeddings
2021
IEEE Transactions on Knowledge and Data Engineering
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In order to exploit the richness of the network context, our model constructs multiple embeddings for each node, each of which captures one modality or type of network information. ...
We then design a late-fusion mechanism to combine the learned embeddings based on the importance of the underlying information. ...
We can summarise our contributions as follows: • We propose NAME (Network Alignment with Multiple Embedding), an end-to-end network alignment framework that learns multiple representations to identify ...
doi:10.1109/tkde.2021.3101840
fatcat:ougfnzoi4ngmdh2genh6mul4mm
Computational prediction and analysis of protein-protein interaction networks
[article]
2017
arXiv
pre-print
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Further, I briefly talk about reconstruction of protein-protein interaction networks by using deep learning. ...
I will discuss several methods for protein-protein interaction network alignment and investigate their preferences to other existing methods. ...
In this thesis, we focus on the task of finding a global alignment between either a pair of or multiple PPI networks. ...
arXiv:1709.01923v2
fatcat:cy63jpklarbmxbrcvfgzx2ob2q
In silico protein function prediction: the rise of machine learning-based approaches
2023
Medical Review
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Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within ...
In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. ...
Bryant et al. used multiple sequence alignment (MSA) as input
Table : Algorithms for protein-protein interactions (PPI) prediction. ...
doi:10.1515/mr-2023-0038
pmid:38282798
pmcid:PMC10808870
fatcat:jw3wlov4jnexddfqywyyxzd4m4
Alignment of Protein Interaction Networks and Disease Prediction: A Survey
2019
International Journal of Advanced Trends in Computer Science and Engineering
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These interactions can be considered as Protein Protein Interaction networks (PPI). Interacting proteins form protein complexes. Mapping nodes between networks is denoted as alignment. ...
In this paper, the authors compared the various aligners, the performance evaluation metrics, the common databases used for PPI evaluation and the importance of PPI network in biomedical research. ...
IsoRankN generates aligned clusters of multiple networks based on spectral clustering [18] .
GRAAL Aligners Graph Alignment Aligners (GRAAL) are based on topological similarity. ...
doi:10.30534/ijatcse/2019/42842019
fatcat:z2id6ephzreuvgjin7rtvi52em
FUSE: Multiple Network Alignment via Data Fusion
[article]
2014
arXiv
pre-print
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We compare FUSE with the state of the art multiple network aligners and show that it produces the largest number of functionally consistent clusters that cover all aligned PPI networks. ...
When we apply NMTF on the five largest and most complete PPI networks from BioGRID, we show that NMTF finds a larger number of protein pairs across the PPI networks that are functionally conserved than ...
In the second part of FUSE, to construct a global one-to-one multiple network alignment, first we construct an edge-weighted k-partite graph, with the proteins of each of the k PPI networks being partitions ...
arXiv:1410.7585v2
fatcat:jfdt3a5ehffyznagy37rksm2si
Interpretable Structured Learning with Sparse Gated Sequence Encoder for Protein-Protein Interaction Prediction
[article]
2020
arXiv
pre-print
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Predicting protein-protein interactions (PPIs) by learning informative representations from amino acid sequences is a challenging yet important problem in biology. ...
Although various deep learning models in Siamese architecture have been proposed to model PPIs from sequences, these methods are computationally expensive for a large number of PPIs due to the pairwise ...
This illustrates that the amino acids in the sequence selectively activated by our model to learn protein representation align with biologically interpretable motifs. ...
arXiv:2010.08514v1
fatcat:npycv5jm6bcyvmpffxzjxippwq
Multimodal Pre-Training Model for Sequence-based Prediction of Protein-Protein Interaction
[article]
2021
arXiv
pre-print
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Pre-training a protein model to learn effective representation is critical for PPIs. ...
Our experiments show that the S2F learns protein embeddings that achieve good performances on a variety of PPIs tasks, including cross-species PPI, antibody-antigen affinity prediction, antibody neutralization ...
On top of the protein representation learned by S2F, we utilize different neural networks based on the Residual RCNN
from PIPR [8] to make the PPI prediction, according to different kinds of PPI tasks: ...
arXiv:2112.04814v1
fatcat:areao63ioncdrf46jr5ty6kdb4
Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions
2022
Frontiers in Microbiology
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convert the learned patterns into final prediction models with high accuracy. ...
Focusing on the recent progresses of deep learning-powered human-virus PPI predictions, we review technical details of these newly developed methods, including dataset preparation, deep learning architectures ...
Considering AF2Complex does not rely on paired multiple sequence alignments, it could be suitable for addressing human-virus PPIs. ...
doi:10.3389/fmicb.2022.842976
pmid:35495666
pmcid:PMC9051481
fatcat:hsbujsdghbeo7fjvn5pta2hjdq
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