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Sun X, Ren X, Zhang J, Nie Y, Hu S, Yang X, Jiang S. Discovering miRNAs Associated With Multiple Sclerosis Based on Network Representation Learning and Deep Learning Methods. Front Genet 2022; 13:899340. [PMID: 35656318 PMCID: PMC9152287 DOI: 10.3389/fgene.2022.899340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/13/2022] [Indexed: 02/02/2023] Open
Abstract
Identifying biomarkers of Multiple Sclerosis is important for the diagnosis and treatment of Multiple Sclerosis. The existing study has shown that miRNA is one of the most important biomarkers for diseases. However, few existing methods are designed for predicting Multiple Sclerosis-related miRNAs. To fill this gap, we proposed a novel computation framework for predicting Multiple Sclerosis-associated miRNAs. The proposed framework uses a network representation model to learn the feature representation of miRNA and uses a deep learning-based model to predict the miRNAs associated with Multiple Sclerosis. The evaluation result shows that the proposed model can predict the miRNAs associated with Multiple Sclerosis precisely. In addition, the proposed model can outperform several existing methods in a large margin.
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Affiliation(s)
- Xiaoping Sun
- Department of Neurology, Zhenhai People's Hospital, Ningbo, China
| | - Xingshuai Ren
- Department of Respiratory, Zouping People's Hospital, Binzhou, China
| | - Jie Zhang
- Department of Neurology, Zouping People's Hospital, Binzhou, China
| | - Yunzhi Nie
- Department of Neurology, Zhenhai People's Hospital, Ningbo, China
| | - Shan Hu
- Nursing Department, Second Sanatorium of Air Force Healthcare Center for Special Services, Hangzhou, China
| | - Xiao Yang
- The Center of Physical Therapy and Rehabilitation, Zhejiang Hospital, Hangzhou, China
| | - Shoufeng Jiang
- Department of Neurology, Shulan Hangzhou Hospital, Hangzhou, China
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2
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Li D, Zhang S, Ma X. Dynamic Module Detection in Temporal Attributed Networks of cancers. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 19:2219-2230. [PMID: 33780342 DOI: 10.1109/tcbb.2021.3069441] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Tracking the dynamic modules during cancer progression is essential for studying cancer pathogenesis, diagnosis and therapy. However, current algorithms only focus on detecting dynamic modules from temporal cancer networks without integrating the heterogeneous genomic data, thereby resulting in undesirable performance. To attack this issue, a novel algorithm (aka TANMF) is proposed to detect dynamic modules in cancer temporal attributed networks, which integrates the temporal networks and gene attributes. To obtain the dynamic modules, the temporality and gene attributed are incorporated into an overall objective function, which transforms the dynamic module detection into an optimization problem. TANMF jointly decomposes the snapshots at two subsequent time steps to obtain the latent features of dynamic modules, where the attributes are fused via regulations. Furthermore, L1 constraint is imposed to improve the robustness. Experimental results demonstrate that TANMF is more accurate than state-of-the-art methods in terms of accuracy. By applying TANMF to breast cancer data, the obtained dynamic modules are more enriched by the known pathways and associated with the survival time of patients. The proposed model and algorithm provide an effective way for the integrative analysis of heterogeneous omics.
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3
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Lin Y, Ma X. Predicting lincRNA-Disease Association in Heterogeneous Networks Using Co-regularized Non-negative Matrix Factorization. Front Genet 2021; 11:622234. [PMID: 33510774 PMCID: PMC7835800 DOI: 10.3389/fgene.2020.622234] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 12/03/2020] [Indexed: 02/02/2023] Open
Abstract
Long intergenic non-coding ribonucleic acids (lincRNAs) are critical regulators for many complex diseases, and identification of disease-lincRNA association is both costly and time-consuming. Therefore, it is necessary to design computational approaches to predict the disease-lincRNA associations that shed light on the mechanisms of diseases. In this study, we develop a co-regularized non-negative matrix factorization (aka Cr-NMF) to identify potential disease-lincRNA associations by integrating the gene expression of lincRNAs, genetic interaction network for mRNA genes, gene-lincRNA associations, and disease-gene associations. The Cr-NMF algorithm factorizes the disease-lincRNA associations, while the other associations/interactions are integrated using regularization. Furthermore, the regularization does not only preserve the topological structure of the lincRNA co-expression network, but also maintains the links “lincRNA → gene → disease.” Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy on predicting the disease-lincRNA associations. The model and algorithm provide an effective way to explore disease-lncRNA associations.
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Affiliation(s)
- Yong Lin
- School of Physics and Electronic Information Engineering, Ningxia Normal University, Guyuan, China
| | - Xiaoke Ma
- School of Computer Science and Technology, Xidian University, Xi'an, China
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4
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Ma X, Sun P, Gong M. An integrative framework of heterogeneous genomic data for cancer dynamic modules based on matrix decomposition. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 19:305-316. [PMID: 32750874 DOI: 10.1109/tcbb.2020.3004808] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Cancer progression is dynamic, and tracking dynamic modules is promising for cancer diagnosis and therapy. Accumulated genomic data provide us an opportunity to investigate the underlying mechanisms of cancers. However, as far as we know, no algorithm has been designed for dynamic modules by integrating heterogeneous omics data. To address this issue, we propose an integrative framework for dynamic module detection based on regularized nonnegative matrix factorization method (DrNMF) by integrating the gene expression and protein interaction network. To remove the heterogeneity of genomic data, we divide the samples of expression profiles into groups to construct gene co-expression networks. To characterize the dynamics of modules, the temporal smoothness framework is adopted, in which the gene co-expression network at the previous stage and protein interaction network are incorporated into the objective function of DrNMF via regularization. The experimental results demonstrate that DrNMF is superior to state-of-the-art methods in terms of accuracy. For breast cancer data, the obtained dynamic modules are more enriched by the known pathways, and can be used to predict the stages of cancers and survival time of patients. The proposed model and algorithm provide an effective integrative analysis of heterogeneous genomic data for cancer progression.
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5
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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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6
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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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7
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Abdur Rahman M, Rashid MM, Le Kernec J, Philippe B, Barnes SJ, Fioranelli F, Yang S, Romain O, Abbasi QH, Loukas G, Imran M. A Secure Occupational Therapy Framework for Monitoring Cancer Patients' Quality of Life. SENSORS (BASEL, SWITZERLAND) 2019; 19:E5258. [PMID: 31795384 PMCID: PMC6928807 DOI: 10.3390/s19235258] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 11/07/2019] [Accepted: 11/22/2019] [Indexed: 02/02/2023]
Abstract
Once diagnosed with cancer, a patient goes through a series of diagnosis and tests, which are referred to as "after cancer treatment". Due to the nature of the treatment and side effects, maintaining quality of life (QoL) in the home environment is a challenging task. Sometimes, a cancer patient's situation changes abruptly as the functionality of certain organs deteriorates, which affects their QoL. One way of knowing the physiological functional status of a cancer patient is to design an occupational therapy. In this paper, we propose a blockchain and off-chain-based framework, which will allow multiple medical and ambient intelligent Internet of Things sensors to capture the QoL information from one's home environment and securely share it with their community of interest. Using our proposed framework, both transactional records and multimedia big data can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient's data available from an in-home secure monitoring system for a better understanding of a patient's current or historical states. Finally, we will present our current implementation status, which provides significant encouragement for further development.
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Affiliation(s)
- Md. Abdur Rahman
- Department of Cyber Security and Forensic Computing, College of Computer and Cyber Sciences (C3S), University of Prince Mugrin, Madinah 41499, Saudi Arabia
| | - Md. Mamunur Rashid
- Consumer and Organisational Digital Analytics (CODA) Research Centre, King’s Business School, King’s College, London WC2B 4BG, UK; (M.M.R.); (S.J.B.)
| | - Julien Le Kernec
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; (J.L.K.); (F.F.); (S.Y.); (Q.H.A.); (M.I.)
- Laboratoire ETIS, Université Paris Seine, Université Cergy-Pontoise, ENSEA, CNRS, UMR8051, 95000 Paris, France;
- School of Information and Communication, University of Electronic, Science, and Technology of China, Chengdu 610000, China
| | - Bruno Philippe
- Pneumology Department, René Dubos Hospital, 95300 Pontoise, France;
| | - Stuart J. Barnes
- Consumer and Organisational Digital Analytics (CODA) Research Centre, King’s Business School, King’s College, London WC2B 4BG, UK; (M.M.R.); (S.J.B.)
| | - Francesco Fioranelli
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; (J.L.K.); (F.F.); (S.Y.); (Q.H.A.); (M.I.)
| | - Shufan Yang
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; (J.L.K.); (F.F.); (S.Y.); (Q.H.A.); (M.I.)
| | - Olivier Romain
- Laboratoire ETIS, Université Paris Seine, Université Cergy-Pontoise, ENSEA, CNRS, UMR8051, 95000 Paris, France;
| | - Qammer H. Abbasi
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; (J.L.K.); (F.F.); (S.Y.); (Q.H.A.); (M.I.)
| | - George Loukas
- Computing and Mathematical Sciences, University of Greenwich, London SE1 09LS, UK;
| | - Muhammad Imran
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; (J.L.K.); (F.F.); (S.Y.); (Q.H.A.); (M.I.)
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8
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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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9
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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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10
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Ma X, Sun P, Zhang ZY. An Integrative Framework for Protein Interaction Network and Methylation Data to Discover Epigenetic Modules. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 16:1855-1866. [PMID: 29994031 DOI: 10.1109/tcbb.2018.2831666] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
DNA methylation is a critical epigenetic modification that plays an important role in cancers. The available algorithms fail to fully characterize epigenetic modules. To address this issue, we first characterize the epigenetic module as a group of well-connected genes in the protein interaction network and are also co-methylated based on gene methylation profiles. Then, the epigenetic module discovery problem is transformed into an optimization problem. Then, a regularized nonnegative matrix factorization algorithm for methylation modules (RNMF-MM) is presented, where the co-methylation constraint is treated as a regularizer. Using the artificial networks with known module structure, we demonstrate that the proposed algorithm outperforms state-of-the-art approaches in terms of accuracy. On the basis of breast cancer methylation data and protein interaction network, the RNMF-MM algorithm discovers methylation modules that are significantly more enriched by the known pathways than those obtained by other algorithms. These modules serve as biomarkers for predicting cancer stages and estimating survival time of patients. The proposed model and algorithm provide an effective way for the integrative analysis of protein interaction network and methylation data.
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11
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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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12
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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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13
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Xi J, Wang M, Li A. Discovering potential driver genes through an integrated model of somatic mutation profiles and gene functional information. MOLECULAR BIOSYSTEMS 2017; 13:2135-2144. [DOI: 10.1039/c7mb00303j] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
An integrated approach to identify driver genes based on information of somatic mutations, the interaction network and Gene Ontology similarity.
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Affiliation(s)
- Jianing Xi
- School of Information Science and Technology
- University of Science and Technology of China
- Hefei AH 230027
- People’s Republic of China
| | - Minghui Wang
- School of Information Science and Technology
- University of Science and Technology of China
- Hefei AH 230027
- People’s Republic of China
- Centers for Biomedical Engineering
| | - Ao Li
- School of Information Science and Technology
- University of Science and Technology of China
- Hefei AH 230027
- People’s Republic of China
- Centers for Biomedical Engineering
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