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Li G, Hu Z, Luo X, Liu J, Wu J, Peng W, Zhu X. Identification of cancer driver genes based on hierarchical weak consensus model. Health Inf Sci Syst 2024; 12:21. [PMID: 38464463 PMCID: PMC10917728 DOI: 10.1007/s13755-024-00279-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 01/31/2024] [Indexed: 03/12/2024] Open
Abstract
Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among features. In this paper, after analyzing a large number of methods for integrating multi-omics data, a hierarchical weak consensus model for fusing multiple features is proposed according to the connection among features. By analyzing the connection between PPI network and co-mutation hypergraph network, this paper firstly proposes a new topological feature, called co-mutation clustering coefficient (CMCC). Then, a hierarchical weak consensus model is used to integrate CMCC, mRNA and miRNA differential expression scores, and a new driver genes identification method HWC is proposed. In this paper, the HWC method and current 7 state-of-the-art methods are compared on three types of cancers. The comparison results show that HWC has the best identification performance in statistical evaluation index, functional consistency and the partial area under ROC curve. Supplementary Information The online version contains supplementary material available at 10.1007/s13755-024-00279-6.
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Affiliation(s)
- Gaoshi Li
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
| | - Zhipeng Hu
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
| | - Xinlong Luo
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
| | - Jiafei Liu
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
| | - Jingli Wu
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
| | - Wei Peng
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500 Yunnan China
| | - Xiaoshu Zhu
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
- School of Computer and Information Security & School of Software Engineering, Guilin University of Electronic Science and Technology, Guilin, China
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Yang X, Che H, Leung MF, Wen S. Self-paced regularized adaptive multi-view unsupervised feature selection. Neural Netw 2024; 175:106295. [PMID: 38614023 DOI: 10.1016/j.neunet.2024.106295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/14/2024] [Accepted: 04/05/2024] [Indexed: 04/15/2024]
Abstract
Multi-view unsupervised feature selection (MUFS) is an efficient approach for dimensional reduction of heterogeneous data. However, existing MUFS approaches mostly assign the samples the same weight, thus the diversity of samples is not utilized efficiently. Additionally, due to the presence of various regularizations, the resulting MUFS problems are often non-convex, making it difficult to find the optimal solutions. To address this issue, a novel MUFS method named Self-paced Regularized Adaptive Multi-view Unsupervised Feature Selection (SPAMUFS) is proposed. Specifically, the proposed approach firstly trains the MUFS model with simple samples, and gradually learns complex samples by using self-paced regularizer. l2,p-norm (0
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Affiliation(s)
- Xuanhao Yang
- College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.
| | - Hangjun Che
- College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China; Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, 400715, China.
| | - Man-Fai Leung
- School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, UK.
| | - Shiping Wen
- Faculty of Engineering and Information Technology, Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia.
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Wang W, Xiao L, Qu G, Calhoun VD, Wang YP, Sun X. Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis. Med Image Anal 2024; 94:103144. [PMID: 38518530 DOI: 10.1016/j.media.2024.103144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 03/17/2024] [Accepted: 03/18/2024] [Indexed: 03/24/2024]
Abstract
Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this paper, we propose a Class-consistency and Site-independence Multiview Hyperedge-Aware HyperGraph Embedding Learning (CcSi-MHAHGEL) framework to integrate FCNs constructed on multiple brain atlases in a multisite fMRI study. Specifically, for each subject, we first model brain network as a hypergraph for every brain atlas to characterize high-order relations among multiple vertexes, and then introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) to extract a multiatlas-based FCN embedding where hyperedge weights are adaptively learned rather than employing the fixed weights precalculated in traditional HGCNs. In addition, we formulate two modules to jointly learn the multiatlas-based FCN embeddings by considering the between-subject associations across classes and sites, respectively, i.e., a class-consistency module to encourage both compactness within every class and separation between classes for promoting discrimination in the embedding space, and a site-independence module to minimize the site dependence of the embeddings for mitigating undesired site influences due to differences in scanning platforms and/or protocols at multiple sites. Finally, the multiatlas-based FCN embeddings are fed into a few fully connected layers followed by the soft-max classifier for diagnosis decision. Extensive experiments on the ABIDE demonstrate the effectiveness of our method for autism spectrum disorder (ASD) identification. Furthermore, our method is interpretable by revealing ASD-relevant brain regions that are biologically significant.
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Affiliation(s)
- Wei Wang
- MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei 230052, China
| | - Li Xiao
- MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei 230052, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China.
| | - Gang Qu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30030, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA
| | - Xiaoyan Sun
- MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei 230052, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
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Liang M, Jiang X, Cao J, Zhang S, Liu H, Li B, Wang L, Zhang C, Jia X. HSG-MGAF Net: Heterogeneous subgraph-guided multiscale graph attention fusion network for interpretable prediction of whole-slide image. Comput Methods Programs Biomed 2024; 247:108099. [PMID: 38442623 DOI: 10.1016/j.cmpb.2024.108099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND AND OBJECTIVE Pathological whole slide image (WSI) prediction and region of interest (ROI) localization are important issues in computer-aided diagnosis and postoperative analysis in clinical applications. Existing computer-aided methods for predicting WSI are mainly based on multiple instance learning (MIL) and its variants. However, most of the methods are based on instance independence and identical distribution assumption and performed at a single scale, which not fully exploit the hierarchical multiscale heterogeneous information contained in WSI. METHODS Heterogeneous Subgraph-Guided Multiscale Graph Attention Fusion Network (HSG-MGAF Net) is proposed to build the topology of critical image patches at two scales for adaptive WSI prediction and lesion localization. The HSG-MGAF Net simulates the hierarchical heterogeneous information of WSI through graph and hypergraph at two scales, respectively. This framework not only fully exploits the low-order and potential high-order correlations of image patches at each scale, but also leverages the heterogeneous information of the two scales for adaptive WSI prediction. RESULTS We validate the superiority of the proposed method on the CAMELYON16 and the TCGA- NSCLC, and the results show that HSG-MGAF Net outperforms the state-of-the-art method on both datasets. The average ACC, AUC and F1 score of HSG-MGAF Net can reach 92.7 %/0.951/0.892 and 92.2 %/0.957/0.919, respectively. The obtained heatmaps can also localize the positive regions more accurately, which have great consistency with the pixel-level labels. CONCLUSIONS The results demonstrate that HSG-MGAF Net outperforms existing weakly supervised learning methods by introducing critical heterogeneous information between the two scales. This approach paves the way for further research on light weighted heterogeneous graph-based WSI prediction and ROI localization.
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Affiliation(s)
- Meiyan Liang
- School of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China.
| | - Xing Jiang
- School of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China
| | - Jie Cao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
| | - Shupeng Zhang
- School of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China
| | - Haishun Liu
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Bo Li
- Department of Rehabilitation Treatment, Shanxi Rongjun Hospital, Taiyuan 030000, China
| | - Lin Wang
- Department of Pathology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan 030032, China
| | - Cunlin Zhang
- Department of physics, Capital Normal University, Beijing 100048, China
| | - Xiaojun Jia
- School of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China
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Song P, Li X, Yuan X, Pang L, Song X, Wang Y. Identifying frequency-dependent imaging genetic associations via hypergraph-structured multi-task sparse canonical correlation analysis. Comput Biol Med 2024; 171:108051. [PMID: 38335819 DOI: 10.1016/j.compbiomed.2024.108051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 01/03/2024] [Accepted: 01/26/2024] [Indexed: 02/12/2024]
Abstract
Identifying complex associations between genetic variations and imaging phenotypes is a challenging task in the research of brain imaging genetics. The previous study has proved that neuronal oscillations within distinct frequency bands are derived from frequency-dependent genetic modulation. Thus it is meaningful to explore frequency-dependent imaging genetic associations, which may give important insights into the pathogenesis of brain disorders. In this work, the hypergraph-structured multi-task sparse canonical correlation analysis (HS-MTSCCA) was developed to explore the associations between multi-frequency imaging phenotypes and single-nucleotide polymorphisms (SNPs). Specifically, we first created a hypergraph for the imaging phenotypes of each frequency and the SNPs, respectively. Then, a new hypergraph-structured constraint was proposed to learn high-order relationships among features in each hypergraph, which can introduce biologically meaningful information into the model. The frequency-shared and frequency-specific imaging phenotypes and SNPs could be identified using the multi-task learning framework. We also proposed a useful strategy to tackle this algorithm and then demonstrated its convergence. The proposed method was evaluated on four simulation datasets and a real schizophrenia dataset. The experimental results on synthetic data showed that HS-MTSCCA outperforms the other competing methods according to canonical correlation coefficients, canonical weights, and cosine similarity. And the results on real data showed that HS-MTSCCA could obtain superior canonical coefficients and canonical weights. Furthermore, the identified frequency-shared and frequency-specific biomarkers could provide more interesting and meaningful information, demonstrating that HS-MTSCCA is a powerful method for brain imaging genetics.
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Affiliation(s)
- Peilun Song
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Xue Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Xiuxia Yuan
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Lijuan Pang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Yaping Wang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China.
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6
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Song C, Liu T, Shi H, Jiao Z. HCTMFS: A multi-modal feature selection framework with higher-order correlated topological manifold for ESRDaMCI. Comput Methods Programs Biomed 2024; 243:107905. [PMID: 37931582 DOI: 10.1016/j.cmpb.2023.107905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/21/2023] [Accepted: 10/27/2023] [Indexed: 11/08/2023]
Abstract
BACKGROUND AND OBJECTIVE The diagnosis of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) mainly relies on objective cognitive assessment, clinical observation, and neuro-psychological evaluation, while only adopting clinical tools often limits the diagnosis accuracy. METHODS We proposed a multi-modal feature selection framework with higher-order correlated topological manifold (HCTMFS) to classify ESRDaMCI patients and identify the discriminative brain regions. It constructed brain structural and functional networks with diffuse kurtosis imaging (DKI) and functional magnetic resonance imaging (fMRI) data, and extracted node efficiency and clustering coefficient from the brain networks to construct multi-modal feature matrices. The topological relationship matrices were constructed to measure the lower-order topological correlation between features. Then the consensus matrices were learned to approximate the topological relationship matrices at different confidence levels and eliminate the noise influence of individual matrices. RESULTS The higher-order topological correlation between features was explored by the Laplacian matrix of the hypergraph, which was calculated through the consensus matrix. The new framework achieved an accuracy rate of 93.56 % for classifying ESRDaMCI patients, and outperformed the existing state-of-the-art methods in terms of sensitivity, specificity, and area under the curve. CONCLUSIONS This study contributes to effectively reflect the functional neural degradation of ESRDaMCI and provide a reference for the diagnosis of ESRDaMCI by selecting discriminative brain regions.
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Affiliation(s)
- Chaofan Song
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
| | - Tongqiang Liu
- Department of Nephrology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Haifeng Shi
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China.
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Ripley DM, Garner T, Hook SA, Veríssimo A, Grunow B, Moritz T, Clayton P, Shiels HA, Stevens A. Warming during embryogenesis induces a lasting transcriptomic signature in fishes. Sci Total Environ 2023; 902:165954. [PMID: 37536606 DOI: 10.1016/j.scitotenv.2023.165954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/24/2023] [Accepted: 07/30/2023] [Indexed: 08/05/2023]
Abstract
Exposure to elevated temperatures during embryogenesis can influence the plasticity of tissues in later life. Despite these long-term changes in plasticity, few differentially expressed genes are ever identified, suggesting that the developmental programming of later life plasticity may occur through the modulation of other aspects of transcriptomic architecture, such as gene network organisation. Here, we use network modelling approaches to demonstrate that warm temperatures during embryonic development (developmental warming) have consistent effects in later life on the organisation of transcriptomic networks across four diverse species of fishes: Scyliorhinus canicula, Danio rerio, Dicentrarchus labrax, and Gasterosteus aculeatus. The transcriptomes of developmentally warmed fishes are characterised by an increased entropy of their pairwise gene interaction networks, implying a less structured, more 'random' set of gene interactions. We also show that, in zebrafish subject to developmental warming, the entropy of an individual gene within a network is associated with that gene's probability of expression change during temperature acclimation in later life. However, this association is absent in animals reared under 'control' conditions. Thus, the thermal environment experienced during embryogenesis can alter transcriptomic organisation in later life, and these changes may influence an individual's responsiveness to future temperature challenges.
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Affiliation(s)
- Daniel M Ripley
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK.
| | - Terence Garner
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Samantha A Hook
- Department of Earth and Environmental Sciences, The University of Manchester, Manchester, UK
| | - Ana Veríssimo
- CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, 4485-661 Vairão, Portugal; BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, 4485-661 Vairão, Portugal
| | - Bianka Grunow
- Fish Growth Physiology, Research Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Timo Moritz
- Deutsches Meeresmuseum, Katharinenberg 14-20, 18439 Stralsund, Germany; Institute of Biological Sciences, University of Rostock, Albert-Einstein-Straße 3, 18059 Rostock, Germany
| | - Peter Clayton
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Holly A Shiels
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Adam Stevens
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK.
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Tsuyuzaki K, Ishii M, Nikaido I. Sctensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data. BMC Bioinformatics 2023; 24:420. [PMID: 37936079 PMCID: PMC10631077 DOI: 10.1186/s12859-023-05490-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/21/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression but the analytical methods are not appropriate to detect many-to-many CCIs. RESULTS In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. CONCLUSIONS Through extensive studies with simulated and empirical datasets, we have shown that scTensor can detect some hypergraphs that cannot be detected using conventional CCI detection methods, especially when they include many-to-many relationships. scTensor is implemented as a freely available R/Bioconductor package.
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Affiliation(s)
- Koki Tsuyuzaki
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
- Japan Science and Technology Agency, PRESTO, 7 Gobancho, Chiyoda-ku, Tokyo, 102-0076, Japan.
| | - Manabu Ishii
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Itoshi Nikaido
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
- Department of Functional Genome Informatics, Division of Biological Data Science, Medical Research Institute, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan.
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Ashraf S, Imran M, Bokhary SAUH, Akhter S. The Wiener index, degree distance index and Gutman index of composite hypergraphs and sunflower hypergraphs. Heliyon 2022; 8:e12382. [PMID: 36578427 PMCID: PMC9791358 DOI: 10.1016/j.heliyon.2022.e12382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/17/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Topological invariants are numerical parameters of graphs or hypergraphs that indicate its topology and are known as graph or hypergraph invariants. In this paper, topological indices of hypergraphs such as Wiener index, degree distance index and Gutman index are considered. A g-composite hypergraphs is a hypergraphs that is obtained by the union of g hypergraphs with every hypergraph has exactly one vertex in common. In this article, results of above said indices for g-composite hypergraphs, where g ≥ 2 , are calculated. Further these results are used to find the Wiener index, degree distance index and Gutman index of sunflower hypergraphs and linear uniform hyper-paths.
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Affiliation(s)
- Sakina Ashraf
- Centre for Advanced Studies in Pure and Applied Mathematics, Bahauddin Zakariya University, Multan, Pakistan
| | - Muhammad Imran
- Department of Mathematical Sciences, College of Sciences, United Arab Emirates University, Al Ain, United Arab Emirates,Corresponding author.
| | - Syed Ahtsham Ul Haq Bokhary
- Centre for Advanced Studies in Pure and Applied Mathematics, Bahauddin Zakariya University, Multan, Pakistan
| | - Shehnaz Akhter
- School of Natural Sciences, National University of Sciences and Technology, H-12, Islamabad, Pakistan
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Abonyi J, Czvetkó T. Hypergraph and network flow-based quality function deployment. Heliyon 2022; 8:e12263. [PMID: 36568657 PMCID: PMC9768306 DOI: 10.1016/j.heliyon.2022.e12263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/02/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
Quality function deployment (QFD) has been a widely-acknowledged tool for translating customer requirements into quality product characteristics based on which product development strategies and focus areas are identified. However, the QFD method considers the correlation and effect between development parameters, but it is not directly implemented in the importance ranking of development actions. Therefore, the cross-relationships between development parameters and their impact on customer requirement satisfaction are often neglected. The primary objective of this study is to make decision-making more reliable by improving QFD with methods that optimize the selection of development parameters even under capacity or cost constraints and directly implement cross-relationships between development parameters and support the identification of interactions visually. Therefore, QFD is accessed from two approaches that proved efficient in operations research. 1) QFD is formulated as a network flow problem with two objectives: maximizing the benefits of satisfying customer needs using linear optimization or minimizing the total cost of actions while still meeting customer requirements using assignment of minimum cost flow approach. 2) QFD is represented as a hypergraph, which allows efficient representation of the interactions of the relationship and correlation matrix and the determination of essential factors based on centrality metrics. The applicability of the methods is demonstrated through an application study in developing a sustainable design of customer electronic products and highlights the improvements' contribution to different development strategies, such as linear optimization performed the best in maximizing customer requirements' satisfaction, assignment as minimum cost flow approach minimized the total cost, while the hypergraph-based representation identified the indirect interactions of development parameters and customer requirements.
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Xu R, Yu Y, Zhang C, Ali MK, Ho JC, Yang C. Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR. Proc Mach Learn Res 2022; 193:259-278. [PMID: 37255863 PMCID: PMC10227831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a novel framework CACHE, to provide effective and insightful clinical predictions based on hypergraph representation learning and counterfactual and factual reasoning techniques. Experiments on two real EHR datasets show the superior performance of CACHE. Case studies with a domain expert illustrate a preferred capability of CACHE in generating clinically meaningful interpretations towards the correct predictions.
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Affiliation(s)
- Ran Xu
- Department of Computer Science, Emory University, Atlanta, GA 30322
| | - Yue Yu
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30332
| | - Chao Zhang
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30332
| | - Mohammed K Ali
- Rollins School of Public Health, Emory University, Atlanta, GA 30322
| | - Joyce C Ho
- Department of Computer Science, Emory University, Atlanta, GA 30322
| | - Carl Yang
- Department of Computer Science, Emory University, Atlanta, GA 30322
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12
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Xie G, Zhu Y, Lin Z, Sun Y, Gu G, Li J, Wang W. HBRWRLDA: predicting potential lncRNA-disease associations based on hypergraph bi-random walk with restart. Mol Genet Genomics 2022; 297:1215-1228. [PMID: 35752742 DOI: 10.1007/s00438-022-01909-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 05/20/2022] [Indexed: 10/17/2022]
Abstract
Accumulating evidence indicates that the regulation of long non-coding RNAs (lncRNAs) is closely related to a variety of diseases. Identifying meaningful lncRNA-disease associations will help to contribute to the understanding of the molecular mechanisms underlying these diseases. However, only a limited number of associations between lncRNAs and diseases have been inferred from traditional biological experiments due to the high cost and highly specialized. Therefore, computational methods are increasingly used to reduce time of biological experiments and complement biological research. In this paper, a computational method called HBRWRLDA is proposed to predict lncRNA-disease associations. First, HBRWRLDA models the relationships between multiple nodes using hypergraphs, which allows HBRWRLDA to integrate the expression similarity of lncRNAs and the semantic similarity of diseases to construct hypergraphs. Then, a bi-random walk on hypergraphs is used to predict potential lncRNA-disease associations. HBRWRLDA achieves a higher area under the curve value of 0.9551 and [Formula: see text], respectively, compared with the other five advanced methods under the framework of one-leave cross validation (LOOCV) and five-fold cross-validation (5-fold CV). In addition, the prediction effect of HBRWRLDA was confirmed case studies of three diseases: renal cell carcinoma, gastric cancer, and hepatocellular carcinoma. Case studies demonstrates the capacity of HBRWRLDA to identify potentially disease-associated lncRNAs. Overall, HBRWRLDA is excellent at predicting potential lncRNA-disease associations and could be useful in conducting further biological experiments by helping researchers identify candidates of lncRNA-disease association.
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Affiliation(s)
- Guobo Xie
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
| | - Yinting Zhu
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
| | - Zhiyi Lin
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China.
| | - Yuping Sun
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
| | - Guosheng Gu
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
| | - Jianming Li
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
| | - Weiming Wang
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
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13
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Rafferty J, Watkins A, Lyons J, Lyons RA, Akbari A, Peek N, Jalali-Najafabadi F, Ba Dhafari T, Pate A, Martin GP, Bailey R. Ranking sets of morbidities using hypergraph centrality. J Biomed Inform 2021; 122:103916. [PMID: 34534697 DOI: 10.1016/j.jbi.2021.103916] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/30/2021] [Accepted: 09/09/2021] [Indexed: 11/24/2022]
Abstract
Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph. We propose the use of hypergraphs, which allows for the incorporation of data on people with any number of conditions, and also allows us to obtain a quantitative understanding of the centrality, a measure of how well connected items in the network are to each other, of both single diseases and sets of conditions. Using this framework we illustrate its application with the set of conditions described in the Charlson morbidity index using data extracted from routinely collected population-scale, patient level electronic health records (EHR) for a cohort of adults in Wales, UK. Stroke and diabetes were found to be the most central single conditions. Sets of diseases featuring diabetes; diabetes with Chronic Pulmonary Disease, Renal Disease, Congestive Heart Failure and Cancer were the most central pairs of diseases. We investigated the differences between results obtained from the hypergraph and a classic binary graph and found that the centrality of diseases such as paraplegia, which are connected strongly to a single other disease is exaggerated in binary graphs compared to hypergraphs. The measure of centrality is derived from the weighting metrics calculated for disease sets and further investigation is needed to better understand the effect of the metric used in identifying the clinical significance and ranked centrality of grouped diseases. These initial results indicate that hypergraphs can be used as a valuable tool for analysing previously poorly understood relationships and information available in EHR data.
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14
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Zhao YY, Jiao CN, Wang ML, Liu JX, Wang J, Zheng CH. HTRPCA: Hypergraph Regularized Tensor Robust Principal Component Analysis for Sample Clustering in Tumor Omics Data. Interdiscip Sci 2021; 14:22-33. [PMID: 34115312 DOI: 10.1007/s12539-021-00441-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 04/27/2021] [Accepted: 05/15/2021] [Indexed: 12/24/2022]
Abstract
In recent years, clustering analysis of cancer genomics data has gained widespread attention. However, limited by the dimensions of the matrix, the traditional methods cannot fully mine the underlying geometric structure information in the data. Besides, noise and outliers inevitably exist in the data. To solve the above two problems, we come up with a new method which uses tensor to represent cancer omics data and applies hypergraph to save the geometric structure information in original data. This model is called hypergraph regularized tensor robust principal component analysis (HTRPCA). The data processed by HTRPCA becomes two parts, one of which is a low-rank component that contains pure underlying structure information between samples, and the other is some sparse interference points. So we can use the low-rank component for clustering. This model can retain complex geometric information between more sample points due to the addition of the hypergraph regularization. Through clustering, we can demonstrate the effectiveness of HTRPCA, and the experimental results on TCGA datasets demonstrate that HTRPCA precedes other advanced methods. This paper proposes a new method of using tensors to represent cancer omics data and introduces hypergraph items to save the geometric structure information of the original data. At the same time, the model decomposes the original tensor into low-order tensors and sparse tensors. The low-rank tensor was used to cluster cancer samples to verify the effectiveness of the method.
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Affiliation(s)
- Yu-Ying Zhao
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Cui-Na Jiao
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Mao-Li Wang
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao, China. .,Rizhao Huilian Zhongchuang Institute of Intelligent Technology, Rizhao, 276826, China.
| | - Juan Wang
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Chun-Hou Zheng
- School of Computer Science, Qufu Normal University, Rizhao, China
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15
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Feng S, Heath E, Jefferson B, Joslyn C, Kvinge H, Mitchell HD, Praggastis B, Eisfeld AJ, Sims AC, Thackray LB, Fan S, Walters KB, Halfmann PJ, Westhoff-Smith D, Tan Q, Menachery VD, Sheahan TP, Cockrell AS, Kocher JF, Stratton KG, Heller NC, Bramer LM, Diamond MS, Baric RS, Waters KM, Kawaoka Y, McDermott JE, Purvine E. Hypergraph models of biological networks to identify genes critical to pathogenic viral response. BMC Bioinformatics 2021; 22:287. [PMID: 34051754 PMCID: PMC8164482 DOI: 10.1186/s12859-021-04197-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 05/13/2021] [Indexed: 12/25/2022] Open
Abstract
Background Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04197-2.
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Affiliation(s)
- Song Feng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Emily Heath
- Department of Mathematics, University of Illinois, Urbana-Champaign, IL, USA
| | - Brett Jefferson
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Cliff Joslyn
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA.,Systems Science Program, Portland State University, Portland, OR, USA
| | - Henry Kvinge
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Hugh D Mitchell
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Brenda Praggastis
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Amie J Eisfeld
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA
| | - Amy C Sims
- Signature Science and Technology Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Larissa B Thackray
- Department of Medicine, Washington University School of Medicine, 63110, Saint Louis, MO, USA
| | - Shufang Fan
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA
| | - Kevin B Walters
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA
| | - Peter J Halfmann
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA
| | - Danielle Westhoff-Smith
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA
| | - Qing Tan
- Department of Medicine, Washington University School of Medicine, 63110, Saint Louis, MO, USA
| | - Vineet D Menachery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, USA
| | - Timothy P Sheahan
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Jacob F Kocher
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Natalie C Heller
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Michael S Diamond
- Department of Medicine, Washington University School of Medicine, 63110, Saint Louis, MO, USA.,Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ralph S Baric
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katrina M Waters
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.,Department of Comparative Medicine, University of Washington, Seattle, WA, USA
| | - Yoshihiro Kawaoka
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA.,Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo, Tokyo, 108-8639, Japan.,ERATO Infection-Induced Host Responses Project, Saitama, 332-0012, Japan.,Department of Special Pathogens, International Research Center for Infectious Diseases, Institute of Medical Science, University of Tokyo, Tokyo, 108-8639, Japan
| | - Jason E McDermott
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.,Department of Molecular Microbiology and Immunology, Oregon Health and Science University, Portland, OR, USA
| | - Emilie Purvine
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA.
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16
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Pradeepa S, Manjula KR. Epidemic zone of COVID-19 from social media using hypergraph with weighting factor (HWF). J Supercomput 2021; 77:11738-11755. [PMID: 33814722 PMCID: PMC8005863 DOI: 10.1007/s11227-021-03726-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/05/2021] [Indexed: 05/13/2023]
Abstract
Online social network is one of the most prominent media that holds information about society's epidemic problem. Due to privacy reasons, most of the users will not disclose their location. Detecting the location of the tweet users is required to track the geographic location of the spreading diseases. This work aims to detect the spreading location of the COVID-19 disease from the Twitter users and content discussed in the tweet. COVID-19 is a disease caused by the "novel coronavirus." About 80% of confirmed cases recover from the disease. However, one out of every six people who get COVID-19 can become seriously ill, stated by the World health organization. Inferring the user location for identifying the spreading location for the disease is a very challenging task. This paper proposes a new technique based on a hypergraph model to detect the Twitter user's locations based on the spreading disease. This model uses hypergraph with weighting factor technique to infer the spreading disease's spatial location. The accuracy of prediction can be improved when a massive volume of streaming data is analyzed. The Helly property of the hypergraph was applied to discard less potential words from the text analysis, which claims this work of unique nature. A weighting factor was introduced to calculate the score of each location for a particular user. The location of each user is predicted based on the one that possesses the highest weighting factor. The proposed framework has been evaluated and tested for various measures like precision, recall and F-measure. The promising results obtained have substantiated the claim for this work compared to the state-of-the-art methodologies.
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Affiliation(s)
- S. Pradeepa
- School of Computing, Sastra Deemed University, Thanjavur, 613401 India
| | - K. R. Manjula
- School of Computing, Sastra Deemed University, Thanjavur, 613401 India
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17
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Meng J, Pitaksirianan N, Tu YC. Counting frequent patterns in large labeled graphs: a hypergraph-based approach. Data Min Knowl Discov 2020; 34:980-1021. [PMID: 38390222 PMCID: PMC10883134 DOI: 10.1007/s10618-020-00686-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 04/15/2020] [Indexed: 11/29/2022]
Abstract
In recent years, the popularity of graph databases has grown rapidly. This paper focuses on single-graph as an effective model to represent information and its related graph mining techniques. In frequent pattern mining in a single-graph setting, there are two main problems: support measure and search scheme. In this paper, we propose a novel framework for designing support measures that brings together existing minimum-image-based and overlap-graph-based support measures. Our framework is built on the concept of occurrence/instance hypergraphs. Based on such, we are able to design a series of new support measures: minimum instance (MI) measure, and minimum vertex cover (MVC) measure, that combine the advantages of existing measures. More importantly, we show that the existing minimum-image-based support measure is an upper bound of the MI measure, which is also linear-time computable and results in counts that are close to number of instances of a pattern. We show that not only most major existing support measures and new measures proposed in this paper can be mapped into the new framework, but also they occupy different locations of the frequency spectrum. By taking advantage of the new framework, we discover that MVC can be approximated to a constant factor (in terms of number of pattern nodes) in polynomial time. In contrast to common belief, we demonstrate that the state-of-the-art overlap-graph-based maximum independent set (MIS) measure also has constant approximation algorithms. We further show that using standard linear programming and semidefinite programming techniques, polynomial-time relaxations for both MVC and MIS measures can be developed and their counts stand between MVC and MIS. In addition, we point out that MVC, MIS, and their relaxations are bounded within constant factor. In summary, all major support measures are unified in the new hypergraph-based framework which helps reveal their bounding relations and hardness properties.
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Affiliation(s)
- Jinghan Meng
- University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620, USA
| | | | - Yi-Cheng Tu
- University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620, USA
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18
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Ghaderi S, Haraldsdóttir HS, Ahookhosh M, Arreckx S, Fleming RMT. Structural conserved moiety splitting of a stoichiometric matrix. J Theor Biol 2020; 499:110276. [PMID: 32333975 DOI: 10.1016/j.jtbi.2020.110276] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 04/03/2020] [Accepted: 04/06/2020] [Indexed: 11/20/2022]
Abstract
Characterising biochemical reaction network structure in mathematical terms enables the inference of functional biochemical consequences from network structure with existing mathematical techniques and spurs the development of new mathematics that exploits the peculiarities of biochemical network structure. The structure of a biochemical network may be specified by reaction stoichiometry, that is, the relative quantities of each molecule produced and consumed in each reaction of the network. A biochemical network may also be specified at a higher level of resolution in terms of the internal structure of each molecule and how molecular structures are transformed by each reaction in a network. The stoichiometry for a set of reactions can be compiled into a stoichiometric matrix N∈Zm×n, where each row corresponds to a molecule and each column corresponds to a reaction. We demonstrate that a stoichiometric matrix may be split into the sum of m-rank(N) moiety transition matrices, each of which corresponds to a subnetwork accessible to a structurally identifiable conserved moiety. The existence of this moiety matrix splitting is a property that distinguishes a stoichiometric matrix from an arbitrary rectangular matrix.
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19
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Ding Q, Shang J, Sun Y, Wang X, Liu JX. HC-HDSD: A method of hypergraph construction and high-density subgraph detection for inferring high-order epistatic interactions. Comput Biol Chem 2018; 78:440-447. [PMID: 30595466 DOI: 10.1016/j.compbiolchem.2018.11.031] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 11/26/2018] [Indexed: 01/08/2023]
Abstract
Detecting epistatic interactions, or nonlinear interactive effects of Single Nucleotide Polymorphisms (SNPs), has gained increasing attention in explaining the "missing heritability" of complex diseases. Though much work has been done in mapping SNPs underlying diseases, most of them constrain to 2-order epistatic interactions. In this paper, a method of hypergraph construction and high-density subgraph detection, named HC-HDSD, is proposed for detecting high-order epistatic interactions. The hypergraph is constructed by low-order epistatic interactions that identified using the normalized co-information measure and the exhaustive search. The hypergraph consists of two types of vertices: real ones representing main effects of SNPs and virtual ones denoting interactive effects of epistatic interactions. Then, both maximal clique centrality algorithm and near-clique mining algorithm are employed to detect high-density subgraphs from the constructed hypergraph. These high-density subgraphs are inferred as high-order epistatic interactions in the HC-HDSD. Experiments are performed on several simulation data sets, results of which show that HC-HDSD is promising in inferring high-order epistatic interactions while substantially reducing the computation cost. In addition, the application of HC-HDSD on a real Age-related Macular Degeneration (AMD) data set provides several new clues for the exploration of causative factors of AMD.
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Affiliation(s)
- Qian Ding
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Junliang Shang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China; School of Statistics, Qufu Normal University, Qufu, 273165, China.
| | - Yingxia Sun
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Xuan Wang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Jin-Xing Liu
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
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20
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Somu N, M R GR, V K, Kirthivasan K, V S SS. An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection. Neural Netw 2018; 108:339-354. [PMID: 30245433 DOI: 10.1016/j.neunet.2018.08.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 07/12/2018] [Accepted: 08/03/2018] [Indexed: 10/28/2022]
Abstract
Trustworthiness is a comprehensive quality metric which is used to assess the quality of the services in service-oriented environments. However, trust prediction of cloud services based on the multi-faceted Quality of Service (QoS) attributes is a challenging task due to the complicated and non-linear relationships between the QoS values and the corresponding trust result. Recent research works reveal the significance of Artificial Neural Network (ANN) and its variants in providing a reasonable degree of success in trust prediction problems. However, the challenges with respect to weight assignment, training time and kernel functions make ANN and its variants under continuous advancements. Hence, this work presents a novel multi-level Hypergraph Coarsening based Robust Heteroscedastic Probabilistic Neural Network (HC-RHRPNN) to predict trustworthiness of cloud services to build high-quality service applications. HC-RHRPNN employs hypergraph coarsening to identify the informative samples, which were then used to train HRPNN to improve its prediction accuracy and minimize the runtime. The performance of HC-RHRPNN was evaluated using Quality of Web Service (QWS) dataset, a public QoS dataset in terms of classifier accuracy, precision, recall, and F-Score.
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Affiliation(s)
- Nivethitha Somu
- Centre for Information Super Highway (CISH), School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India.
| | - Gauthama Raman M R
- Centre for Information Super Highway (CISH), School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India.
| | - Kalpana V
- Centre for Information Super Highway (CISH), School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India.
| | - Kannan Kirthivasan
- Discrete Mathematics Research Laboratory (DMRL), Department of Mathematics, SASTRA Deemed University, Thanjavur, Tamil Nadu, India.
| | - Shankar Sriram V S
- Centre for Information Super Highway (CISH), School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India.
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21
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Wu ZY, He J, Liu YM, Tian JK. An upper bound for the Z-spectral radius of adjacency tensors. J Inequal Appl 2018; 2018:76. [PMID: 29657511 PMCID: PMC5889425 DOI: 10.1186/s13660-018-1672-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 04/03/2018] [Indexed: 06/08/2023]
Abstract
Let [Formula: see text] be a k-uniform hypergraph on n vertices with degree sequence [Formula: see text]. In this paper, in terms of degree [Formula: see text], we give a new upper bound for the Z-spectral radius of the adjacency tensor of [Formula: see text]. Some examples are given to show the efficiency of the bound.
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Affiliation(s)
- Zhi-Yong Wu
- School of Mathematics, Zunyi Normal College, Zunyi, P.R. China
| | - Jun He
- School of Mathematics, Zunyi Normal College, Zunyi, P.R. China
| | - Yan-Min Liu
- School of Mathematics, Zunyi Normal College, Zunyi, P.R. China
| | - Jun-Kang Tian
- School of Mathematics, Zunyi Normal College, Zunyi, P.R. China
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22
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Abstract
In synthesis planning, the goal is to synthesize a target molecule from available starting materials, possibly optimizing costs such as price or environmental impact of the process. Current algorithmic approaches to synthesis planning are usually based on selecting a bond set and finding a single good plan among those induced by it. We demonstrate that synthesis planning can be phrased as a combinatorial optimization problem on hypergraphs by modeling individual synthesis plans as directed hyperpaths embedded in a hypergraph of reactions (HoR) representing the chemistry of interest. As a consequence, a polynomial time algorithm to find the K shortest hyperpaths can be used to compute the K best synthesis plans for a given target molecule. Having K good plans to choose from has many benefits: it makes the synthesis planning process much more robust when in later stages adding further chemical detail, it allows one to combine several notions of cost, and it provides a way to deal with imprecise yield estimates. A bond set gives rise to a HoR in a natural way. However, our modeling is not restricted to bond set based approaches—any set of known reactions and starting materials can be used to define a HoR. We also discuss classical quality measures for synthesis plans, such as overall yield and convergency, and demonstrate that convergency has a built-in inconsistency which could render its use in synthesis planning questionable. Decalin is used as an illustrative example of the use and implications of our results.![]()
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Affiliation(s)
- Rolf Fagerberg
- Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark
| | - Christoph Flamm
- Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Vienna, Austria
| | - Rojin Kianian
- Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark.,Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, Härtelstraße 16-18, 04107, Leipzig, Germany.,Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany
| | - Daniel Merkle
- Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark
| | - Peter F Stadler
- Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Vienna, Austria. .,Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, Härtelstraße 16-18, 04107, Leipzig, Germany. .,Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany. .,Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, 04103, Leipzig, Germany. .,Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark. .,Santa Fe Institute, 1399 Hyde Park Rd, 87501, Santa Fe, USA.
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23
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Swaminathan V, Rajaram G, Abhishek V, Reddy BS, Kannan K. A Novel Hypergraph-Based Genetic Algorithm (HGGA) Built on Unimodular and Anti-homomorphism Properties for DNA Sequencing by Hybridization. Interdiscip Sci 2017; 11:397-411. [PMID: 29110287 DOI: 10.1007/s12539-017-0267-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2017] [Revised: 10/11/2017] [Accepted: 10/16/2017] [Indexed: 11/29/2022]
Abstract
The sequencing by hybridization (SBH) of determining the order in which nucleotides should occur on a DNA string is still under discussion for enhancements on computational intelligence although the next generation of DNA sequencing has come into existence. In the last decade, many works related to graph theory-based DNA sequencing have been carried out in the literature. This paper proposes a method for SBH by integrating hypergraph with genetic algorithm (HGGA) for designing a novel analytic technique to obtain DNA sequence from its spectrum. The paper represents elements of the spectrum and its relation as hypergraph and applies the unimodular property to ensure the compatibility of relations between l-mers. The hypergraph representation and unimodular property are bound with the genetic algorithm that has been customized with a novel selection and crossover operator reducing the computational complexity with accelerated convergence. Subsequently, upon determining the primary strand, an anti-homomorphism is invoked to find the reverse complement of the sequence. The proposed algorithm is implemented in the GenBank BioServer datasets, and the results are found to prove the efficiency of the algorithm. The HGGA is a non-classical algorithm with significant advantages and computationally attractive complexity reductions ranging to [Formula: see text] with improved accuracy that makes it prominent for applications other than DNA sequencing like image processing, task scheduling and big data processing.
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Affiliation(s)
- V Swaminathan
- Discrete Mathematics Research Laboratory, Srinivasa Ramanujan Centre, SASTRA University, Thanjavur, India.,School of Humanities and Sciences, SASTRA University, Thanjavur, India
| | - Gangothri Rajaram
- School of Computing, SASTRA University, Thanjavur, Tamilnadu, India. .,School of Humanities and Sciences, SASTRA University, Thanjavur, India.
| | - V Abhishek
- School of Computing, SASTRA University, Thanjavur, Tamilnadu, India.,School of Humanities and Sciences, SASTRA University, Thanjavur, India
| | - Boosi Shashank Reddy
- School of Computing, SASTRA University, Thanjavur, Tamilnadu, India.,School of Humanities and Sciences, SASTRA University, Thanjavur, India
| | - K Kannan
- Discrete Mathematics Research Laboratory, Srinivasa Ramanujan Centre, SASTRA University, Thanjavur, India. .,School of Computing, SASTRA University, Thanjavur, Tamilnadu, India. .,School of Humanities and Sciences, SASTRA University, Thanjavur, India.
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Shang J, Sun Y, Liu JX, Xia J, Zhang J, Zheng CH. CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions. BMC Bioinformatics 2016; 17:214. [PMID: 27184783 DOI: 10.1186/s12859-016-1076-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 05/07/2016] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Detecting and visualizing nonlinear interaction effects of single nucleotide polymorphisms (SNPs) or epistatic interactions are important topics in bioinformatics since they play an important role in unraveling the mystery of "missing heritability". However, related studies are almost limited to pairwise epistatic interactions due to their methodological and computational challenges. RESULTS We develop CINOEDV (Co-Information based N-Order Epistasis Detector and Visualizer) for the detection and visualization of epistatic interactions of their orders from 1 to n (n ≥ 2). CINOEDV is composed of two stages, namely, detecting stage and visualizing stage. In detecting stage, co-information based measures are employed to quantify association effects of n-order SNP combinations to the phenotype, and two types of search strategies are introduced to identify n-order epistatic interactions: an exhaustive search and a particle swarm optimization based search. In visualizing stage, all detected n-order epistatic interactions are used to construct a hypergraph, where a real vertex represents the main effect of a SNP and a virtual vertex denotes the interaction effect of an n-order epistatic interaction. By deeply analyzing the constructed hypergraph, some hidden clues for better understanding the underlying genetic architecture of complex diseases could be revealed. CONCLUSIONS Experiments of CINOEDV and its comparison with existing state-of-the-art methods are performed on both simulation data sets and a real data set of age-related macular degeneration. Results demonstrate that CINOEDV is promising in detecting and visualizing n-order epistatic interactions. CINOEDV is implemented in R and is freely available from R CRAN: http://cran.r-project.org and https://sourceforge.net/projects/cinoedv/files/ .
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Abstract
Mathematical modelling of epidemic propagation on networks is extended to hypergraphs in order to account for both the community structure and the nonlinear dependence of the infection pressure on the number of infected neighbours. The exact master equations of the propagation process are derived for an arbitrary hypergraph given by its incidence matrix. Based on these, moment closure approximation and mean-field models are introduced and compared to individual-based stochastic simulations. The simulation algorithm, developed for networks, is extended to hypergraphs. The effects of hypergraph structure and the model parameters are investigated via individual-based simulation results.
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Marashi SA, Tefagh M. A mathematical approach to emergent properties of metabolic networks: partial coupling relations, hyperarcs and flux ratios. J Theor Biol 2014; 355:185-93. [PMID: 24751930 DOI: 10.1016/j.jtbi.2014.04.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2013] [Revised: 03/07/2014] [Accepted: 04/14/2014] [Indexed: 01/12/2023]
Abstract
Emergent properties in systems biology are those which arise only when the biological system passes a certain level of complexity. In this study, we introduce some of the emergent properties which appear in the constraint-based analysis of metabolic networks. These properties generally appear as a result of existence of hfdeyperarcs and irreversible reactions in networks. Here, we present examples of metabolic networks in which there exist at least two reactions whose fluxes cannot be written as products and/or ratios of the stoichiometric coefficients of the network. We show that any such network contains at least one hyperarc. Additionally, we prove that partial coupling cannot appear in consistent metabolic networks with less than four reactions, or with less than three irreversible reactions, or without hyperarc(s).
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Affiliation(s)
- Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
| | - Mojtaba Tefagh
- Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran.
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Breuer F, Dall A, Kubitzke M. Hypergraph coloring complexes. Discrete Math 2012; 312:2407-2420. [PMID: 23483700 PMCID: PMC3587399 DOI: 10.1016/j.disc.2012.04.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2011] [Revised: 04/28/2012] [Accepted: 04/28/2012] [Indexed: 06/01/2023]
Abstract
The aim of this paper is to generalize the notion of the coloring complex of a graph to hypergraphs. We present three different interpretations of those complexes-a purely combinatorial one and two geometric ones. It is shown, that most of the properties, which are known to be true for coloring complexes of graphs, break down in this more general setting, e.g., Cohen-Macaulayness and partitionability. Nevertheless, we are able to provide bounds for the [Formula: see text]- and [Formula: see text]-vectors of those complexes which yield new bounds on chromatic polynomials of hypergraphs. Moreover, though it is proven that the coloring complex of a hypergraph has a wedge decomposition, we provide an example showing that in general this decomposition is not homotopy equivalent to a wedge of spheres. In addition, we can completely characterize those hypergraphs whose coloring complex is connected.
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Affiliation(s)
- Felix Breuer
- Fachbereich Mathematik und Informatik, Freie Universität Berlin, Arnimallee 3, D-14195 Berlin, Germany
| | - Aaron Dall
- Departament de Matemàtica Aplicada II, Universitat Politècnica de Catalunya, Jordi Girona 1-3, E-08034 Barcelona, Spain
| | - Martina Kubitzke
- Fakultät für Mathematik, Universität Wien, Garnisongasse 3, A-1090 Wien, Austria
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