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Liu Y, Li A, Zeng A, Zhou J, Fan Y, Di Z. Motif-based community detection in heterogeneous multilayer networks. Sci Rep 2024; 14:8769. [PMID: 38627531 PMCID: PMC11021438 DOI: 10.1038/s41598-024-59120-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/08/2024] [Indexed: 04/19/2024] Open
Abstract
Multilayer networks composed of intralayer edges and interlayer edges are an important type of complex networks. Considering the heterogeneity of nodes and edges, it is necessary to design more reasonable and diverse community detection methods for multilayer networks. Existing research on community detection in multilayer networks mainly focuses on multiplexing networks (where the nodes are homogeneous and the edges are heterogeneous), but few studies have focused on heterogeneous multilayer networks where both nodes and edges represent different semantics. In this paper, we studied community detection on heterogeneous multilayer networks and proposed a motif-based detection algorithm. First, the communities and motifs of multilayer networks are defined, especially the interlayer motifs. Then, the modularity of multilayer networks based on these motifs is designed, and the community structure of the multilayer network is detected by maximizing the modularity of multilayer networks. Finally, we verify the effectiveness of the detection algorithm on synthetic networks. In the experiments on synthetic networks, comparing with the classical community detection algorithms (without considering interlayer heterogeneity), the motif-based modularity community detection algorithm can obtain better results under different evaluation indexes, and we found that there exists a certain relationship between motifs and communities. In addition, the proposed algorithm is applied in the empirical network, which shows its practicability in the real world. This study provides a solution for the investigation of heterogeneous information in multilayer networks.
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Affiliation(s)
- Yafang Liu
- School of Systems Science, Beijing Normal University, Beijing, 100875, People's Republic of China
| | - Aiwen Li
- School of Systems Science, Beijing Normal University, Beijing, 100875, People's Republic of China
| | - An Zeng
- School of Systems Science, Beijing Normal University, Beijing, 100875, People's Republic of China
| | - Jianlin Zhou
- School of Systems Science, Beijing Normal University, Beijing, 100875, People's Republic of China.
| | - Ying Fan
- School of Systems Science, Beijing Normal University, Beijing, 100875, People's Republic of China.
| | - Zengru Di
- School of Systems Science, Beijing Normal University, Beijing, 100875, People's Republic of China
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2
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Núñez-Carpintero I, Rigau M, Bosio M, O'Connor E, Spendiff S, Azuma Y, Topf A, Thompson R, 't Hoen PAC, Chamova T, Tournev I, Guergueltcheva V, Laurie S, Beltran S, Capella-Gutiérrez S, Cirillo D, Lochmüller H, Valencia A. Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes. Nat Commun 2024; 15:1227. [PMID: 38418480 PMCID: PMC10902324 DOI: 10.1038/s41467-024-45099-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 01/15/2024] [Indexed: 03/01/2024] Open
Abstract
Exploring the molecular basis of disease severity in rare disease scenarios is a challenging task provided the limitations on data availability. Causative genes have been described for Congenital Myasthenic Syndromes (CMS), a group of diverse minority neuromuscular junction (NMJ) disorders; yet a molecular explanation for the phenotypic severity differences remains unclear. Here, we present a workflow to explore the functional relationships between CMS causal genes and altered genes from each patient, based on multilayer network community detection analysis of complementary biomedical information provided by relevant data sources, namely protein-protein interactions, pathways and metabolomics. Our results show that CMS severity can be ascribed to the personalized impairment of extracellular matrix components and postsynaptic modulators of acetylcholine receptor (AChR) clustering. This work showcases how coupling multilayer network analysis with personalized -omics information provides molecular explanations to the varying severity of rare diseases; paving the way for sorting out similar cases in other rare diseases.
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Affiliation(s)
- Iker Núñez-Carpintero
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, 08034, Barcelona, Spain
| | - Maria Rigau
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, 08034, Barcelona, Spain
- MRC London Institute of Medical Sciences, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Mattia Bosio
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, 08034, Barcelona, Spain
- Coordination Unit Spanish National Bioinformatics Institute (INB/ELIXIR-ES), Barcelona Supercomputing Center, Barcelona, Spain
| | - Emily O'Connor
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Sally Spendiff
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Yoshiteru Azuma
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Japan
- Department of Pediatrics, Aichi Medical University, Nagakute, Japan
| | - Ana Topf
- John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Rachel Thompson
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Peter A C 't Hoen
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Nijmegen, The Netherlands
| | - Teodora Chamova
- Department of Neurology, Expert Centre for Hereditary Neurologic and Metabolic Disorders, Alexandrovska University Hospital, Medical University-Sofia, Sofia, Bulgaria
| | - Ivailo Tournev
- Department of Neurology, Expert Centre for Hereditary Neurologic and Metabolic Disorders, Alexandrovska University Hospital, Medical University-Sofia, Sofia, Bulgaria
- Department of Cognitive Science and Psychology, New Bulgarian University, Sofia, 1618, Bulgaria
| | - Velina Guergueltcheva
- Clinic of Neurology, University Hospital Sofiamed, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
| | - Steven Laurie
- Centro Nacional de Análisis Genómico (CNAG-CRG), Center for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Catalonia, Spain
| | - Sergi Beltran
- Centro Nacional de Análisis Genómico (CNAG-CRG), Center for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona (UB), Barcelona, Spain
| | - Salvador Capella-Gutiérrez
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, 08034, Barcelona, Spain
- Coordination Unit Spanish National Bioinformatics Institute (INB/ELIXIR-ES), Barcelona Supercomputing Center, Barcelona, Spain
| | - Davide Cirillo
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, 08034, Barcelona, Spain.
| | - Hanns Lochmüller
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
- Centro Nacional de Análisis Genómico (CNAG-CRG), Center for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Catalonia, Spain
- Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, ON, Canada
- Department of Neuropediatrics and Muscle Disorders, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, 08034, Barcelona, Spain
- Coordination Unit Spanish National Bioinformatics Institute (INB/ELIXIR-ES), Barcelona Supercomputing Center, Barcelona, Spain
- ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain
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3
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Wu S, Jin K, Tang M, Xia Y, Gao W. Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs. Interdiscip Sci 2024:10.1007/s12539-024-00604-3. [PMID: 38342857 DOI: 10.1007/s12539-024-00604-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/26/2023] [Accepted: 01/03/2024] [Indexed: 02/13/2024]
Abstract
Since gene regulation is a complex process in which multiple genes act simultaneously, accurately inferring gene regulatory networks (GRNs) is a long-standing challenge in systems biology. Although graph neural networks can formally describe intricate gene expression mechanisms, current GRN inference methods based on graph learning regard only transcription factor (TF)-target gene interactions as pairwise relationships, and cannot model the many-to-many high-order regulatory patterns that prevail among genes. Moreover, these methods often rely on limited prior regulatory knowledge, ignoring the structural information of GRNs in gene expression profiles. Therefore, we propose a multi-view hierarchical hypergraphs GRN (MHHGRN) inference model. Specifically, multiple heterogeneous biological information is integrated to construct multi-view hierarchical hypergraphs of TFs and target genes, using hypergraph convolution networks to model higher order complex regulatory relationships. Meanwhile, the coupled information diffusion mechanism and the cross-domain messaging mechanism facilitate the information sharing between genes to optimise gene embedding representations. Finally, a unique channel attention mechanism is used to adaptively learn feature representations from multiple views for GRN inference. Experimental results show that MHHGRN achieves better results than the baseline methods on the E. coli and S. cerevisiae benchmark datasets of the DREAM5 challenge, and it has excellent cross-species generalization, achieving comparable or better performance on scRNA-seq datasets from five mouse and two human cell lines.
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Affiliation(s)
- Songyang Wu
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
| | - Kui Jin
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
| | - Mingjing Tang
- School of Life Science, Yunnan Normal University, Kunming, 650500, China.
- Engineering Research Center of Sustainable Development and Utilization of Biomass Energy, Ministry of Education, Yunnan Normal University, Kunming, 650500, China.
| | - Yuelong Xia
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
| | - Wei Gao
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
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4
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Pio-Lopez L, Levin M. Morphoceuticals: perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer and aging. Drug Discov Today 2023; 28:103585. [PMID: 37059328 DOI: 10.1016/j.drudis.2023.103585] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/18/2023] [Accepted: 04/06/2023] [Indexed: 04/16/2023]
Abstract
Morphoceuticals are a new class of interventions that target the setpoints of anatomical homeostasis for efficient, modular control of growth and form. Here, we focus on a subclass: electroceuticals, which specifically target the cellular bioelectrical interface. Cellular collectives in all tissues form bioelectrical networks via ion channels and gap junctions that process morphogenetic information, controlling gene expression and allowing cell networks to adaptively and dynamically control growth and pattern formation. Recent progress in understanding this physiological control system, including predictive computational models, suggests that targeting bioelectrical interfaces can control embryogenesis and maintain shape against injury, senescence and tumorigenesis. We propose a roadmap for drug discovery focused on manipulating endogenous bioelectric signaling for regenerative medicine, cancer suppression and antiaging therapeutics. Teaser: By taking advantage of the native problem-solving competencies of cells and tissues, a new kind of top-down approach to biomedicine becomes possible. Bioelectricity offers an especially tractable interface for interventions targeting the software of life for regenerative medicine applications.
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Affiliation(s)
- Léo Pio-Lopez
- Allen Discovery Center, Tufts University, Medford, MA, USA
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
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5
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Feature-rich multiplex lexical networks reveal mental strategies of early language learning. Sci Rep 2023; 13:1474. [PMID: 36702869 PMCID: PMC9879964 DOI: 10.1038/s41598-022-27029-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 12/23/2022] [Indexed: 01/27/2023] Open
Abstract
Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms-fragmented across linguistics, psychology and computer science-by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children's syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Biased random walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.
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6
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Zhang Y, Xiang J, Tang L, Yang J, Li J. PGAGP: Predicting pathogenic genes based on adaptive network embedding algorithm. Front Genet 2023; 13:1087784. [PMID: 36744177 PMCID: PMC9895109 DOI: 10.3389/fgene.2022.1087784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/09/2022] [Indexed: 01/21/2023] Open
Abstract
The study of disease-gene associations is an important topic in the field of computational biology. The accumulation of massive amounts of biomedical data provides new possibilities for exploring potential relations between diseases and genes through computational strategy, but how to extract valuable information from the data to predict pathogenic genes accurately and rapidly is currently a challenging and meaningful task. Therefore, we present a novel computational method called PGAGP for inferring potential pathogenic genes based on an adaptive network embedding algorithm. The PGAGP algorithm is to first extract initial features of nodes from a heterogeneous network of diseases and genes efficiently and effectively by Gaussian random projection and then optimize the features of nodes by an adaptive refining process. These low-dimensional features are used to improve the disease-gene heterogenous network, and we apply network propagation to the improved heterogenous network to predict pathogenic genes more effectively. By a series of experiments, we study the effect of PGAGP's parameters and integrated strategies on predictive performance and confirm that PGAGP is better than the state-of-the-art algorithms. Case studies show that many of the predicted candidate genes for specific diseases have been implied to be related to these diseases by literature verification and enrichment analysis, which further verifies the effectiveness of PGAGP. Overall, this work provides a useful solution for mining disease-gene heterogeneous network to predict pathogenic genes more effectively.
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Affiliation(s)
- Yan Zhang
- School of Computer Science and Engineering, Central South University, Changsha, China
- School of Information Science and Engineering, Changsha Medical University, Changsha, China
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Ju Xiang
- School of Computer Science and Engineering, Central South University, Changsha, China
- School of Information Science and Engineering, Changsha Medical University, Changsha, China
- Academician Workstation, Changsha Medical University, Changsha, China
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China
- Department of Basic Medical Sciences and Neuroscience Research Center, Changsha Medical University, Changsha, China
| | - Liang Tang
- Academician Workstation, Changsha Medical University, Changsha, China
- Department of Basic Medical Sciences and Neuroscience Research Center, Changsha Medical University, Changsha, China
| | - Jialiang Yang
- Academician Workstation, Changsha Medical University, Changsha, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
- Geneis Beijing Co., Ltd, Beijing, China
| | - Jianming Li
- Academician Workstation, Changsha Medical University, Changsha, China
- Department of Basic Medical Sciences and Neuroscience Research Center, Changsha Medical University, Changsha, China
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7
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Jagtap S, Çelikkanat A, Pirayre A, Bidard F, Duval L, Malliaros FD. BraneMF: integration of biological networks for functional analysis of proteins. Bioinformatics 2022; 38:5383-5389. [PMID: 36321881 DOI: 10.1093/bioinformatics/btac691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 10/05/2022] [Accepted: 11/01/2022] [Indexed: 11/11/2022] Open
Abstract
MOTIVATION The cellular system of a living organism is composed of interacting bio-molecules that control cellular processes at multiple levels. Their correspondences are represented by tightly regulated molecular networks. The increase of omics technologies has favored the generation of large-scale disparate data and the consequent demand for simultaneously using molecular and functional interaction networks: gene co-expression, protein-protein interaction (PPI), genetic interaction and metabolic networks. They are rich sources of information at different molecular levels, and their effective integration is essential to understand cell functioning and their building blocks (proteins). Therefore, it is necessary to obtain informative representations of proteins and their proximity, that are not fully captured by features extracted directly from a single informational level. We propose BraneMF, a novel random walk-based matrix factorization method for learning node representation in a multilayer network, with application to omics data integration. RESULTS We test BraneMF with PPI networks of Saccharomyces cerevisiae, a well-studied yeast model organism. We demonstrate the applicability of the learned features for essential multi-omics inference tasks: clustering, function and PPI prediction. We compare it to the state-of-the-art integration methods for multilayer networks. BraneMF outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks. The robustness of results is assessed by an extensive parameter sensitivity analysis. AVAILABILITY AND IMPLEMENTATION BraneMF's code is freely available at: https://github.com/Surabhivj/BraneMF, along with datasets, embeddings and result files. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Surabhi Jagtap
- IFP Energies Nouvelles, 92852 Rueil-Malmaison, France.,Université Paris-Saclay, CentraleSupélec, Inria, Centre for Visual Computing, 91190 Gif-Sur-Yvette, France
| | | | | | | | - Laurent Duval
- IFP Energies Nouvelles, 92852 Rueil-Malmaison, France
| | - Fragkiskos D Malliaros
- Université Paris-Saclay, CentraleSupélec, Inria, Centre for Visual Computing, 91190 Gif-Sur-Yvette, France
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Hiort P, Hugo J, Zeinert J, Müller N, Kashyap S, Rajapakse JC, Azuaje F, Renard BY, Baum K. DrDimont: explainable drug response prediction from differential analysis of multi-omics networks. Bioinformatics 2022; 38:ii113-ii119. [PMID: 36124784 PMCID: PMC9486584 DOI: 10.1093/bioinformatics/btac477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. RESULTS We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response. AVAILABILITY AND IMPLEMENTATION DrDimont is available on CRAN: https://cran.r-project.org/package=DrDimont. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Pauline Hiort
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Julian Hugo
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Justus Zeinert
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Nataniel Müller
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Spoorthi Kashyap
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Jagath C Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | | | - Bernhard Y Renard
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
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9
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HOPLP − MUL: link prediction in multiplex networks based on higher order paths and layer fusion. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03733-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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10
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Sonawane AR, Aikawa E, Aikawa M. Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases. Front Cardiovasc Med 2022; 9:873582. [PMID: 35665246 PMCID: PMC9160390 DOI: 10.3389/fcvm.2022.873582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/19/2022] [Indexed: 01/18/2023] Open
Abstract
Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.
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Affiliation(s)
- Abhijeet Rajendra Sonawane
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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11
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MTGK: Multi-source cross-network node classification via transferable graph knowledge. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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12
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Yang Z, Cheng J. Recommendation algorithm based on attributed multiplex heterogeneous network. PeerJ Comput Sci 2021; 7:e822. [PMID: 35036537 PMCID: PMC8725661 DOI: 10.7717/peerj-cs.822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 11/29/2021] [Indexed: 06/14/2023]
Abstract
In the field of deep learning, the processing of large network models on billions or even tens of billions of nodes and numerous edge types is still flawed, and the accuracy of recommendations is greatly compromised when large network embeddings are applied to recommendation systems. To solve the problem of inaccurate recommendations caused by processing deficiencies in large networks, this paper combines the attributed multiplex heterogeneous network with the attention mechanism that introduces the softsign and sigmoid function characteristics and derives a new framework SSN_GATNE-T (S represents the softsign function, SN represents the attention mechanism introduced by the Softsign function, and GATNE-T represents the transductive embeddings learning for attribute multiple heterogeneous networks). The attributed multiplex heterogeneous network can help obtain more user-item information with more attributes. No matter how many nodes and types are included in the model, our model can handle it well, and the improved attention mechanism can help annotations to obtain more useful information via a combination of the two. This can help to mine more potential information to improve the recommendation effect; in addition, the application of the softsign function in the fully connected layer of the model can better reduce the loss of potential user information, which can be used for accurate recommendation by the model. Using the Adam optimizer to optimize the model can not only make our model converge faster, but it is also very helpful for model tuning. The proposed framework SSN_GATNE-T was tested for two different types of datasets, Amazon and YouTube, using three evaluation indices, ROC-AUC (receiver operating characteristic-area under curve), PR-AUC (precision recall-area under curve) and F1 (F1-score), and found that SSN_GATNE-T improved on all three evaluation indices compared to the mainstream recommendation models currently in existence. This not only demonstrates that the framework can deal well with the shortcomings of obtaining accurate interaction information due to the presence of a large number of nodes and edge types of the embedding of large network models, but also demonstrates the effectiveness of addressing the shortcomings of large networks to improve recommendation performance. In addition, the model is also a good solution to the cold start problem.
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