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MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach
2021
Scientific Reports
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MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous ...
Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. ...
Our illustration of MultiVERSE embedding to study gene-disease associations could easily be applied to drug repositioning and drug discovery, for instance with a multiplex drug-drug network, a drug-target ...
doi:10.1038/s41598-021-87987-1
pmid:33888761
pmcid:PMC8062697
fatcat:bfq774o4wrfhbnsm3ou3lic4wi
Prediction of Herb-Target based on Representation Learning of Symptom related Heterogeneous Network
2019
Computational and Structural Biotechnology Journal
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In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. ...
HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous ...
Our method applies a network-embedding algorithm, called node2vec [22] , to encode the heterogeneous network associated with herbs and targets by interconnecting the phenotypes (i.e. symptoms and diseases ...
doi:10.1016/j.csbj.2019.02.002
pmid:30867892
pmcid:PMC6396098
fatcat:3gu5uxzqofhuxkivmmmjy7aroi
MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2008.10085v1 fatcat:3xsi3nanbvbupd5r3sxwjrx24a
MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in the task of link prediction ...
MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. ...
interactions between drugs and their target proteins [61] . ...
arXiv:2008.10085v2
fatcat:deingnkayvbxtajrzodsktifbu
SkipGNN: predicting molecular interactions with skip-graph networks
2020
Scientific Reports
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Experiments on four interaction networks, including drug–drug, drug–target, protein–protein, and gene–disease interactions, show that SkipGNN achieves superior and robust performance. ...
In biological networks, however, similarity between nodes that do not directly interact has proved incredibly useful in the last decade across a variety of interaction networks. ...
We start by evaluating SkipGNN on four distinct types of molecular interactions, including drug-target interactions, drug-drug interactions, protein-protein interactions, and gene-disease interactions, ...
doi:10.1038/s41598-020-77766-9
pmid:33273494
fatcat:7335w7rqcba6jnypepwsbn7gg4
Leveraging Distributed Biomedical Knowledge Sources to Discover Novel Uses for Known Drugs
[article]
2019
bioRxiv
pre-print
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We then employ a graph node embedding scheme and use utilize a random forest model to make novel predictions about which drugs can be used to treat certain diseases. ...
networks. ...
Machine learning techniques have also been put forth in the context of network-based inference of drug-target interaction. ...
doi:10.1101/765305
fatcat:sipul2nwxfbyfars2usguoqj7q
An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network
2022
BMC Bioinformatics
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More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. ...
Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. ...
Based on the above, in this paper, we constructed a molecular association network (MAN), including miRNA, lncRNA, protein, drug, disease, and nine associations (lncRNAprotein interaction [10] , drug-protein ...
doi:10.1186/s12859-021-04553-2
pmid:34983364
pmcid:PMC8726520
fatcat:x3vmu2sc2bckjczlps7wut3334
Novel drug-target interactions via link prediction and network embedding
2022
BMC Bioinformatics
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Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. ...
It maps drug-drug and protein–protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target ...
Recently, node2vec showed promising results on DTI prediction by mapping drug, protein, disease, lncRNA and miRNA association networks to vectors [44] . ...
doi:10.1186/s12859-022-04650-w
pmid:35379165
pmcid:PMC8978405
fatcat:glk7nxpwsjhfhev222vksoc4sa
Deep learning integration of molecular and interactome data for protein-compound interaction prediction
2021
Journal of Cheminformatics
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However, there have been few attempts to combine both types of data in molecular information and interaction networks. ...
Existing machine learning methods for predicting protein-compound interactions are largely divided into those based on molecular structure data and those based on network data. ...
Usage: (1) data pre-processing, (2) application of node2vec to multi-interactome data, (3) learning and prediction by the integrated model.
Mesulergine ...
doi:10.1186/s13321-021-00513-3
pmid:33933121
pmcid:PMC8088618
fatcat:4j2nalermbefpp6wd2bjautui4
Pathway and network embedding methods for prioritizing psychiatric drugs
[article]
2019
bioRxiv
pre-print
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Using protein-protein- interaction networks and embedding-based methods, we build a pipeline to prioritize treatments for psychiatric diseases that achieves a 3.4-fold improvement over a background model ...
Moreover, network-based models informed by gene expression can represent pathological biological mechanisms and suggest new genes for diagnosis and treatment. ...
methods, we used node2vec embeddings to capture structural features of the PPI networks and to inform our drug ranking lists by disease. ...
doi:10.1101/728055
fatcat:6qa7h5e6sjcjlfvvmupztwm3ey
Integrative Construction and Analysis of Molecular Association Network in Human Cells by fusing Node Attribute and Behavior Information
2019
Molecular Therapy: Nucleic Acids
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Inspired by holism, a machine-learning-based framework called MAN-node2vec is proposed to predict multi-type relationships in the molecular associations network (MAN). ...
Then, each biomolecule in MAN can be represented as a vector by its attribute learned by k-mer, etc. and its behavior learned by node2vec. ...
Cheng et al. 15 infer new targets for known drugs only through drug-target bipartite network topology similarity. ...
doi:10.1016/j.omtn.2019.10.046
pmid:31923739
pmcid:PMC6951835
fatcat:nmweqhhtkvddnhpluv6hhjnnbe
A Node Embedding Framework for Integration of Similarity-based Drug Combination Prediction
[article]
2020
arXiv
pre-print
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Results: In this paper, we proposed a Network Embedding framework in Multiplex Networks (NEMN) to predict synthetic drug combinations. ...
For Drug combination prediction, we found seven novel drug combinations which have been validated by external sources among the top-ranked predictions of our model. ...
Such as Drug-Target interactions prediction, Drug-Disease interactions prediction. ...
arXiv:2002.10625v1
fatcat:i4zemlxopfgqpje247hoclte4q
Modeling Pharmacological Effects with Multi-Relation Unsupervised Graph Embedding
[article]
2020
arXiv
pre-print
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A pharmacological effect of a drug on cells, organs and systems refers to the specific biochemical interaction produced by a drug substance, which is called its mechanism of action. ...
found on recent biomedical literature that were also predicted by our method. ...
These representations were then engaged in the prediction of drug-target interactions. Yamanishi et al. ...
arXiv:2004.14842v2
fatcat:xsyhjkho7jesndalsvh47cfye4
Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus
2021
Frontiers in Genetics
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Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. ...
With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. ...
, drug targets (Peng et al., 2021c), drug side-effects (Han et al., 2021), RNA-targets (Peng et al., 2019b), molecular network edges (Perozzi et al., 2014; Ribeiro et al., 2017; Peng et al., 2021d), etc ...
doi:10.3389/fgene.2021.779186
pmid:34899863
pmcid:PMC8657768
fatcat:h7ziptxhwbcjze5cbmtv2k4v2i
Recent Advances in Network-based Methods for Disease Gene Prediction
[article]
2020
arXiv
pre-print
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Since molecular networks are able to capture complex interplay among molecules in diseases, they become one of the most extensively used data for disease-gene association prediction. ...
In this survey, we aim to provide a comprehensive and an up-to-date review of network-based methods for disease gene prediction. We also conduct an empirical analysis on 14 state-of-the-art methods. ...
In particular, they further select 11 graph embedding methods and perform a systematic evaluation on 5 different tasks, i.e., drug-disease association prediction, drug-drug interaction prediction, PPI ...
arXiv:2007.10848v1
fatcat:zhrspbsj6zfpfhwa42mzjp4lvy
Pathway and network embedding methods for prioritizing psychiatric drugs
2020
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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Using protein-protein-interaction networks and embedding-based methods, we build a pipeline to prioritize treatments for psychiatric diseases that achieves a 3.4-fold improvement over a background model ...
Moreover, network-based models informed by gene expression can represent pathological biological mechanisms and suggest new genes for diagnosis and treatment. ...
Acknowledgements and Code Availability is supported by GM102365. Our code is available at https://github.com/ypershad/pathway-network-psych-drugs. ...
pmid:31797637
pmcid:PMC6951442
fatcat:7dyk34uslzgk5imwu6nnumedki
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