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Peng S, Yang M, Yang Z, Chen T, Xie J, Ma G. A weighted prior tensor train decomposition method for community detection in multi-layer networks. Neural Netw 2024; 179:106523. [PMID: 39053300 DOI: 10.1016/j.neunet.2024.106523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 06/12/2024] [Accepted: 07/06/2024] [Indexed: 07/27/2024]
Abstract
Community detection in multi-layer networks stands as a prominent subject within network analysis research. However, the majority of existing techniques for identifying communities encounter two primary constraints: they lack suitability for high-dimensional data within multi-layer networks and fail to fully leverage additional auxiliary information among communities to enhance detection accuracy. To address these limitations, a novel approach named weighted prior tensor training decomposition (WPTTD) is proposed for multi-layer network community detection. Specifically, the WPTTD method harnesses the tensor feature optimization techniques to effectively manage high-dimensional data in multi-layer networks. Additionally, it employs a weighted flattened network to construct prior information for each dimension of the multi-layer network, thereby continuously exploring inter-community connections. To preserve the cohesive structure of communities and to harness comprehensive information within the multi-layer network for more effective community detection, the common community manifold learning (CCML) is integrated into the WPTTD framework for enhancing the performance. Experimental evaluations conducted on both artificial and real-world networks have verified that this algorithm outperforms several mainstream multi-layer network community detection algorithms.
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Affiliation(s)
- Siyuan Peng
- School of Information Engineering, Guangdong University of Technology, 510006, China
| | - Mingliang Yang
- School of Information Engineering, Guangdong University of Technology, 510006, China
| | - Zhijing Yang
- School of Information Engineering, Guangdong University of Technology, 510006, China.
| | - Tianshui Chen
- School of Information Engineering, Guangdong University of Technology, 510006, China
| | - Jieming Xie
- School of Information Engineering, Guangdong University of Technology, 510006, China
| | - Guang Ma
- Department of Computer Science, University of York, YO105DD, England, United Kingdom
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2
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Wu W, Ma X, Wang Q, Gong M, Gao Q. Learning deep representation and discriminative features for clustering of multi-layer networks. Neural Netw 2024; 170:405-416. [PMID: 38029721 DOI: 10.1016/j.neunet.2023.11.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/29/2023] [Accepted: 11/22/2023] [Indexed: 12/01/2023]
Abstract
The multi-layer network consists of the interactions between different layers, where each layer of the network is depicted as a graph, providing a comprehensive way to model the underlying complex systems. The layer-specific modules of multi-layer networks are critical to understanding the structure and function of the system. However, existing methods fail to characterize and balance the connectivity and specificity of layer-specific modules in networks because of the complicated inter- and intra-coupling of various layers. To address the above issues, a joint learning graph clustering algorithm (DRDF) for detecting layer-specific modules in multi-layer networks is proposed, which simultaneously learns the deep representation and discriminative features. Specifically, DRDF learns the deep representation with deep nonnegative matrix factorization, where the high-order topology of the multi-layer network is gradually and precisely characterized. Moreover, it addresses the specificity of modules with discriminative feature learning, where the intra-class compactness and inter-class separation of pseudo-labels of clusters are explored as self-supervised information, thereby providing a more accurate method to explicitly model the specificity of the multi-layer network. Finally, DRDF balances the connectivity and specificity of layer-specific modules with joint learning, where the overall objective of the graph clustering algorithm and optimization rules are derived. The experiments on ten multi-layer networks showed that DRDF not only outperforms eight baselines on graph clustering but also enhances the robustness of algorithms.
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Affiliation(s)
- Wenming Wu
- School of Computer Science and Technology, Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, 710071, China
| | - Xiaoke Ma
- School of Computer Science and Technology, Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, 710071, China.
| | - Quan Wang
- School of Computer Science and Technology, Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, 710071, China
| | - Maoguo Gong
- School of Electronic Engineering, Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, 710071, China
| | - Quanxue Gao
- School of Telecommunication, Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, 710071, China
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3
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Wu W, Yang T, Ma X, Zhang W, Li H, Huang J, Li Y, Cui J. Learning Specific and Conserved Features of Multi-layer Networks. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.11.150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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4
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Embedding regularized nonnegative matrix factorization for structural reduction in multi-layer networks. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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5
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Wang Y, Ma X. Joint nonnegative matrix factorization and network embedding for graph co-clustering. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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6
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Li D, Zhong X, Dou Z, Gong M, Ma X. Detecting dynamic community by fusing network embedding and nonnegative matrix factorization. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106961] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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7
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Savino A, Provero P, Poli V. Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression. Int J Mol Sci 2020; 21:E9461. [PMID: 33322692 PMCID: PMC7764314 DOI: 10.3390/ijms21249461] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/02/2020] [Accepted: 12/09/2020] [Indexed: 02/02/2023] Open
Abstract
Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes' mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.
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Affiliation(s)
- Aurora Savino
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
| | - Paolo Provero
- Department of Neurosciences “Rita Levi Montalcini”, University of Turin, Corso Massimo D’Ázeglio 52, 10126 Turin, Italy;
- Center for Omics Sciences, Ospedale San Raffaele IRCCS, Via Olgettina 60, 20132 Milan, Italy
| | - Valeria Poli
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
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8
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Osorio D, Zhong Y, Li G, Huang JZ, Cai JJ. scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data. PATTERNS (NEW YORK, N.Y.) 2020; 1:100139. [PMID: 33336197 PMCID: PMC7733883 DOI: 10.1016/j.patter.2020.100139] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 09/29/2020] [Accepted: 10/12/2020] [Indexed: 02/02/2023]
Abstract
We present scTenifoldNet-a machine learning workflow built upon principal-component regression, low-rank tensor approximation, and manifold alignment-for constructing and comparing single-cell gene regulatory networks (scGRNs) using data from single-cell RNA sequencing. scTenifoldNet reveals regulatory changes in gene expression between samples by comparing the constructed scGRNs. With real data, scTenifoldNet identifies specific gene expression programs associated with different biological processes, providing critical insights into the underlying mechanism of regulatory networks governing cellular transcriptional activities.
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Affiliation(s)
- Daniel Osorio
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA
| | - Yan Zhong
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - Guanxun Li
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - Jianhua Z. Huang
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - James J. Cai
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
- Interdisciplinary Program of Genetics, Texas A&M University, College Station, TX 77843, USA
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9
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Liu H, Guan J, Li H, Bao Z, Wang Q, Luo X, Xue H. Predicting the Disease Genes of Multiple Sclerosis Based on Network Representation Learning. Front Genet 2020; 11:328. [PMID: 32373160 PMCID: PMC7186413 DOI: 10.3389/fgene.2020.00328] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 03/19/2020] [Indexed: 02/02/2023] Open
Abstract
Multiple sclerosis (MS) is an autoimmune disease for which it is difficult to find exact disease-related genes. Effectively identifying disease-related genes would contribute to improving the treatment and diagnosis of multiple sclerosis. Current methods for identifying disease-related genes mainly focus on the hypothesis of guilt-by-association and pay little attention to the global topological information of the whole protein-protein-interaction (PPI) network. Besides, network representation learning (NRL) has attracted a huge amount of attention in the area of network analysis because of its promising performance in node representation and many downstream tasks. In this paper, we try to introduce NRL into the task of disease-related gene prediction and propose a novel framework for identifying the disease-related genes multiple sclerosis. The proposed framework contains three main steps: capturing the topological structure of the PPI network using NRL-based methods, encoding learned features into low-dimensional space using a stacked autoencoder, and training a support vector machine (SVM) classifier to predict disease-related genes. Compared with three state-of-the-art algorithms, our proposed framework shows superior performance on the task of predicting disease-related genes of multiple sclerosis.
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Affiliation(s)
- Haijie Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Department of Physical Medicine and Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China
- Stroke Biological Recovery Laboratory, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, The Teaching Affiliate of Harvard Medical School Charlestown, Boston, MA, United States
| | - Jiaojiao Guan
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - He Li
- Department of Automation, College of Information Science and Engineering, Tianjin Tianshi College, Tianjin, China
| | - Zhijie Bao
- School of Textile Science and Engineering, Tiangong University, Tianjin, China
| | - Qingmei Wang
- Stroke Biological Recovery Laboratory, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, The Teaching Affiliate of Harvard Medical School Charlestown, Boston, MA, United States
| | - Xun Luo
- Kerry Rehabilitation Medicine Research Institute, Shenzhen, China
- Shenzhen Dapeng New District Nan'ao People's Hospital, Shenzhen, China
| | - Hansheng Xue
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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10
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Di Nanni N, Bersanelli M, Milanesi L, Mosca E. Network Diffusion Promotes the Integrative Analysis of Multiple Omics. Front Genet 2020; 11:106. [PMID: 32180795 PMCID: PMC7057719 DOI: 10.3389/fgene.2020.00106] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 01/29/2020] [Indexed: 02/01/2023] Open
Abstract
The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the mathematical machinery of network diffusion—also referred to as network propagation—has been exploited in several network-based pipelines, thanks to its ability of amplifying association between genes that lie in network proximity. Indeed, network diffusion provides a quantitative estimation of network proximity between genes associated with one or more different data types, from simple binary vectors to real vectors. Therefore, this powerful data transformation method has also been increasingly used in integrative analyses of multiple collections of biological scores and/or one or more interaction networks. We present an overview of the state of the art of bioinformatics pipelines that use network diffusion processes for the integrative analysis of omics data. We discuss the fundamental ways in which network diffusion is exploited, open issues and potential developments in the field. Current trends suggest that network diffusion is a tool of broad utility in omics data analysis. It is reasonable to think that it will continue to be used and further refined as new data types arise (e.g. single cell datasets) and the identification of system-level patterns will be considered more and more important in omics data analysis.
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Affiliation(s)
- Noemi Di Nanni
- Institute of Biomedical Technologies, National Research Council, Milan, Italy.,Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy
| | - Matteo Bersanelli
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy.,National Institute of Nuclear Physics (INFN), Bologna, Italy
| | - Luciano Milanesi
- Institute of Biomedical Technologies, National Research Council, Milan, Italy
| | - Ettore Mosca
- Institute of Biomedical Technologies, National Research Council, Milan, Italy
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11
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Lin Q, Lin Y, Yu Q, Ma X. Clustering of Cancer Attributed Networks via Integration of Graph Embedding and Matrix Factorization. IEEE ACCESS 2020. [DOI: 10.1109/access.2020.3034623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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12
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Detecting evolving communities in dynamic networks using graph regularized evolutionary nonnegative matrix factorization. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019. [DOI: 10.1016/j.physa.2019.121279] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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13
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Ma X, Dong D, Wang Q. Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2019. [DOI: 10.1109/tkde.2018.2832205] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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14
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Identifying Cancer Specific Driver Modules Using a Network-Based Method. Molecules 2018; 23:molecules23051114. [PMID: 29738475 PMCID: PMC6100049 DOI: 10.3390/molecules23051114] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 04/26/2018] [Accepted: 05/07/2018] [Indexed: 02/01/2023] Open
Abstract
Detecting driver modules is a key challenge for understanding the mechanisms of carcinogenesis at the pathway level. Identifying cancer specific driver modules is helpful for interpreting the different principles of different cancer types. However, most methods are proposed to identify driver modules in one cancer, but few methods are introduced to detect cancer specific driver modules. We propose a network-based method to detect cancer specific driver modules (CSDM) in a certain cancer type to other cancer types. We construct the specific network of a cancer by combining specific coverage and mutual exclusivity in all cancer types, to catch the specificity of the cancer at the pathway level. To illustrate the performance of the method, we apply CSDM on 12 TCGA cancer types. When we compare CSDM with SpeMDP and HotNet2 with regard to specific coverage and the enrichment of GO terms and KEGG pathways, CSDM is more accurate. We find that the specific driver modules of two different cancers have little overlap, which indicates that the driver modules detected by CSDM are specific. Finally, we also analyze three specific driver modules of BRCA, BLCA, and LAML intersecting with well-known pathways. The source code of CSDM is freely accessible at https://github.com/fengli28/CSDM.git.
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15
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Zhang E, Ma X. Regularized Multi-View Subspace Clustering for Common Modules Across Cancer Stages. Molecules 2018; 23:molecules23051016. [PMID: 29701681 PMCID: PMC6102576 DOI: 10.3390/molecules23051016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 04/23/2018] [Accepted: 04/23/2018] [Indexed: 02/01/2023] Open
Abstract
Discovering the common modules that are co-expressed across various stages can lead to an improved understanding of the underlying molecular mechanisms of cancers. There is a shortage of efficient tools for integrative analysis of gene expression and protein interaction networks for discovering common modules associated with cancer progression. To address this issue, we propose a novel regularized multi-view subspace clustering (rMV-spc) algorithm to obtain a representation matrix for each stage and a joint representation matrix that balances the agreement across various stages. To avoid the heterogeneity of data, the protein interaction network is incorporated into the objective of rMV-spc via regularization. Based on the interior point algorithm, we solve the optimization problem to obtain the common modules. By using artificial networks, we demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy. Furthermore, the rMV-spc discovers common modules in breast cancer networks based on the breast data, and these modules serve as biomarkers to predict stages of breast cancer. The proposed model and algorithm effectively integrate heterogeneous data for dynamic modules.
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Affiliation(s)
- Enli Zhang
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China.
| | - Xiaoke Ma
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China.
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16
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Multi-Objective Optimization Algorithm to Discover Condition-Specific Modules in Multiple Networks. Molecules 2017; 22:molecules22122228. [PMID: 29240706 PMCID: PMC6149918 DOI: 10.3390/molecules22122228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 12/10/2017] [Accepted: 12/11/2017] [Indexed: 02/02/2023] Open
Abstract
The advances in biological technologies make it possible to generate data for multiple conditions simultaneously. Discovering the condition-specific modules in multiple networks has great merit in understanding the underlying molecular mechanisms of cells. The available algorithms transform the multiple networks into a single objective optimization problem, which is criticized for its low accuracy. To address this issue, a multi-objective genetic algorithm for condition-specific modules in multiple networks (MOGA-CSM) is developed to discover the condition-specific modules. By using the artificial networks, we demonstrate that the MOGA-CSM outperforms state-of-the-art methods in terms of accuracy. Furthermore, MOGA-CSM discovers stage-specific modules in breast cancer networks based on The Cancer Genome Atlas (TCGA) data, and these modules serve as biomarkers to predict stages of breast cancer. The proposed model and algorithm provide an effective way to analyze multiple networks.
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