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Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations [article]

Lei Deng, Yibiao Huang, Xuejun Liu, Hui Liu
2021 arXiv   pre-print
We constructed multi-modal attributed graphs based on multiple features of microbes and drugs, such as molecular structures, microbe genetic sequences, and function annotations.  ...  Taking as input the multi-modal attribute graphs, VGAE was trained to learn the informative and interpretable latent representations of each node and the whole graph, and then a deep neural network classifier  ...  This work was supported by the National Natural Science Foundation of China under grants No. 61972422 and No. 62072058. Conflict of interest: none declared.  ... 
arXiv:2108.06338v1 fatcat:a55iqgfpbra2ll5i3hzo5rwt6a

Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction [article]

Yingheng Wang, Yaosen Min, Xin Chen, Ji Wu
2020 arXiv   pre-print
This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity, to capture inter-view molecule structure and intra-view  ...  We use GCNs and bond-aware attentive message passing networks to encode DDI relationships and drug molecular graphs in the MIRACLE learning stage, respectively.  ...  [28] uses attentive graph auto-encoders [19] to integrates heterogeneous information from divergent drug-related data sources.  ... 
arXiv:2010.11711v2 fatcat:ryzrp7rosnblhc3bpcgnrmvxp4

ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy [article]

Annie Wang, Hansaim Lim, Shu-Yuan Cheng, Lei Xie
2017 bioRxiv   pre-print
with Restart and can assess the reliability of a specific prediction.  ...  Our finding demonstrates the power of big data analytics in drug discovery, and has a great potential toward developing a targeted therapy for the effective treatment of TNBC.  ...  Center for Advancing Translational Sciences of NIH, and Grant Number MD007599 from the National Institute on Minority Health and Health Disparities (NIMHD) of NIH.  ... 
doi:10.1101/192385 fatcat:3fmr4f2p3vcplfv6qie46fsz6m

Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning

Maryam Lotfi Shahreza, Nasser Ghadiri, Seyed Rasoul Mousavi, Jaleh Varshosaz, James R. Green
2017 Journal of Biomedical Informatics  
Although previous studies have made a strong case for the effectiveness of integrative network-based methods for predicting these interrelationships, little progress has been achieved in this regard within  ...  Our algorithm integrates these various types of data into a heterogeneous network and implements a label propagation algorithm to find new interactions.  ...  The PREDICT [40] algorithm also used drug side effects to rank drugdisease associations. There is some limitation for using of side-effect.  ... 
doi:10.1016/j.jbi.2017.03.006 pmid:28300647 fatcat:nfs7y4lcyjcixam3ijv6ghcokm

An Ensemble Approach Based on Multi-Source Information to Predict Drug-MiRNA Associations via Convolutional Neural Networks

K. Deepthi, A. S. Jereesh
2021 IEEE Access  
The results and case studies illustrate the effectiveness of ELDMA in identifying novel drug-miRNA candidates. The top predicted relationships are released for future wet-lab studies.  ...  The method constructed features based on the integrated pairwise similarities of drugs and miRNAs and reduced the feature dimensions with principal component analysis (PCA).  ...  based on the presumption that drugs that share common side effects tend to be similar.  ... 
doi:10.1109/access.2021.3063885 fatcat:kkytemn5dndirowhg3il5vdize

Graph Representation Learning in Biomedicine [article]

Michelle M. Li, Kexin Huang, Marinka Zitnik
2022 arXiv   pre-print
Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge.  ...  We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces.  ...  Acknowledgements We gratefully acknowledge the support of NSF under Nos. IIS-and IIS-, US Air Force Contract No.  ... 
arXiv:2104.04883v3 fatcat:lrhxlztborbylazvdfmaxk5zem

Novel drug target identification for the treatment of dementia using multi-relational association mining

Thanh-Phuong Nguyen, Corrado Priami, Laura Caberlotto
2015 Scientific Reports  
Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein  ...  We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic  ...  Acknowledgments We are grateful to Bianca Baldacci for helping us to improve the quality of the figures.  ... 
doi:10.1038/srep11104 pmid:26154857 pmcid:PMC4495601 fatcat:nl46vll2u5dqtkrvhzjowg4giy

Graph Neural Networks and Their Current Applications in Bioinformatics

Xiao-Meng Zhang, Li Liang, Lin Liu, Ming-Jing Tang
2021 Frontiers in Genetics  
Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging.  ...  Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation.  ...  All authors have read and agreed to the published version of the manuscript.  ... 
doi:10.3389/fgene.2021.690049 fatcat:4p55ap6sivcy7h6dpne5fut6lu

Compact Integration of Multi-Network Topology for Functional Analysis of Genes

Hyunghoon Cho, Bonnie Berger, Jian Peng
2016 Cell Systems  
Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node.  ...  We performed 5-fold cross-validation to compare the function prediction performance of Mashup to other state-of-the-art network integration methods, GeneMANIA and STRING's Bayesian integration followed  ...  and demonstrated that analyzing the topology of a PPI network alone can be effective for predicting genetic interactions.  ... 
doi:10.1016/j.cels.2016.10.017 pmid:27889536 pmcid:PMC5225290 fatcat:kbcyts5cavb3vmtrocyjziww2m

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

Mohieddin Jafari, Yinyin Wang, Ali Amiryousefi, Jing Tang
2020 Frontiers in Pharmacology  
We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.  ...  The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis.  ...  Mehrdad Karimi for his helpful comments, and Ehsan Zanganeh and Minoo Ashtiani for graphical designing of Figure 1 .  ... 
doi:10.3389/fphar.2020.01319 pmid:32982738 pmcid:PMC7479204 fatcat:uec3v3d5mzgy7fctde3ueriigi

Systems biology visualization tools for drug target discovery

Tianxiao Huan, Xiaogang Wu, Jake Y Chen
2010 Expert Opinion on Drug Discovery  
Acknowledgments The authors thank M Grobe of the Pervasive Technology Institute at Indiana University for proofreading the manuscript. 650 Declaration of interest This work is partly supported by the  ...  Department of Defense (DOD) Breast Cancer Research Program (BCRP) Concept Award (W81XWH-08-1-0623) to J Chen.  ...  The quality of these perspectives further depends on the quality of analysis of complicating biological pathways, related drug targets, companion biomarkers and optimized chemical compounds with common  ... 
doi:10.1517/17460441003725102 pmid:22823128 fatcat:3gumecrgwjafrkgzxcn753sbhu

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

Zheng Gao, Gang Fu, Chunping Ouyang, Satoshi Tsutsui, Xiaozhong Liu, Jeremy Yang, Christopher Gessner, Brian Foote, David Wild, Ying Ding, Qi Yu
2019 BMC Bioinformatics  
Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs.  ...  Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains.  ...  For example, Chem2Bio2RDF [11] integrates over 25 different datasets related to drug discovery and comprises a large scale heterogeneous network.  ... 
doi:10.1186/s12859-019-2914-2 fatcat:6oiu3qoi7ncbppiaetzwqx6zxm

Providing data science support for systems pharmacology and its implications to drug discovery

Thomas Hart, Lei Xie
2016 Expert Opinion on Drug Discovery  
The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery  ...  deconvolution and personalized adverse drug reaction prediction.  ...  Acknowledgements We sincerely thank the editor and the reviewers for their constructive suggestions This work was supported by the National Library of Medicine of the National Institute of Health under  ... 
doi:10.1517/17460441.2016.1135126 pmid:26689499 pmcid:PMC4988863 fatcat:ol5ra4b2efewtiietrqhcocqle

Network-based machine learning and graph theory algorithms for precision oncology

Wei Zhang, Jeremy Chien, Jeongsik Yong, Rui Kuang
2017 npj Precision Oncology  
This article reviews networkbased machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms  ...  , candidate targets and repositioned drugs for personalized treatment.  ...  The dbGaP accession number to the specific version of the TCGA dataset is phs000178.v9.p8. This research work is supported by a grant from the National Science Foundations, USA (NSF III 1149697).  ... 
doi:10.1038/s41698-017-0029-7 pmid:29872707 pmcid:PMC5871915 fatcat:yqeb4ntx7rgy3g5yep53u57wgq

Disease networks and their contribution to disease understanding and drug repurposing. A survey of the state of the art [article]

Eduardo Garcia del Valle, Gerardo Lagunes Garcia, Lucia Prieto Santamaria, Massimiliano Zanin, Ernestina Menasalvas Ruiz, Alejandro Rodriguez Gonzalez
2018 bioRxiv   pre-print
This information can be very valuable for the generation of new prediction models based on disease networks.  ...  One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing.  ...  The available information includes side effect frequency, drug and side effect classifications as well as links to further information, for example drugtarget relations. http://www.pharmgkb.org Pharmacogenomics  ... 
doi:10.1101/415257 fatcat:rahsf6ugdfcytnatt6i7jqjxaq
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