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Gravel B, Renaux A, Papadimitriou S, Smits G, Nowé A, Lenaerts T. Prioritization of oligogenic variant combinations in whole exomes. Bioinformatics 2024; 40:btae184. [PMID: 38603604 PMCID: PMC11037482 DOI: 10.1093/bioinformatics/btae184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 01/29/2024] [Accepted: 04/10/2024] [Indexed: 04/13/2024] Open
Abstract
MOTIVATION Whole exome sequencing (WES) has emerged as a powerful tool for genetic research, enabling the collection of a tremendous amount of data about human genetic variation. However, properly identifying which variants are causative of a genetic disease remains an important challenge, often due to the number of variants that need to be screened. Expanding the screening to combinations of variants in two or more genes, as would be required under the oligogenic inheritance model, simply blows this problem out of proportion. RESULTS We present here the High-throughput oligogenic prioritizer (Hop), a novel prioritization method that uses direct oligogenic information at the variant, gene and gene pair level to detect digenic variant combinations in WES data. This method leverages information from a knowledge graph, together with specialized pathogenicity predictions in order to effectively rank variant combinations based on how likely they are to explain the patient's phenotype. The performance of Hop is evaluated in cross-validation on 36 120 synthetic exomes for training and 14 280 additional synthetic exomes for independent testing. Whereas the known pathogenic variant combinations are found in the top 20 in approximately 60% of the cross-validation exomes, 71% are found in the same ranking range when considering the independent set. These results provide a significant improvement over alternative approaches that depend simply on a monogenic assessment of pathogenicity, including early attempts for digenic ranking using monogenic pathogenicity scores. AVAILABILITY AND IMPLEMENTATION Hop is available at https://github.com/oligogenic/HOP.
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Affiliation(s)
- Barbara Gravel
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Department of Computer Science, Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Department of Computer Science, Artificial Intelligence Laboratory, Vrije Universiteit Brussels, 1050 Brussels, Belgium
| | - Alexandre Renaux
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Department of Computer Science, Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Department of Computer Science, Artificial Intelligence Laboratory, Vrije Universiteit Brussels, 1050 Brussels, Belgium
| | - Sofia Papadimitriou
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Department of Computer Science, Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Brussels Interuniversity Genomics High Throughput core (BRIGHTcore), UZ Brussel, Vrije Universiteit Brussel (VUB) - Université Libre de Bruxelles (ULB), 1090 Brussels, Belgium
| | - Guillaume Smits
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Center of Human Genetics, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Ann Nowé
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Department of Computer Science, Artificial Intelligence Laboratory, Vrije Universiteit Brussels, 1050 Brussels, Belgium
| | - Tom Lenaerts
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Department of Computer Science, Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Department of Computer Science, Artificial Intelligence Laboratory, Vrije Universiteit Brussels, 1050 Brussels, Belgium
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Yu S, Wang Z, Nan J, Li A, Yang X, Tang X. Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation. JMIR Form Res 2023; 7:e50998. [PMID: 37966892 PMCID: PMC10687686 DOI: 10.2196/50998] [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: 07/18/2023] [Revised: 09/28/2023] [Accepted: 10/27/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Schizophrenia is a serious mental disease. With increased research funding for this disease, schizophrenia has become one of the key areas of focus in the medical field. Searching for associations between diseases and genes is an effective approach to study complex diseases, which may enhance research on schizophrenia pathology and lead to the identification of new treatment targets. OBJECTIVE The aim of this study was to identify potential schizophrenia risk genes by employing machine learning methods to extract topological characteristics of proteins and their functional roles in a protein-protein interaction (PPI)-keywords (PPIK) network and understand the complex disease-causing property. Consequently, a PPIK-based metagraph representation approach is proposed. METHODS To enrich the PPI network, we integrated keywords describing protein properties and constructed a PPIK network. We extracted features that describe the topology of this network through metagraphs. We further transformed these metagraphs into vectors and represented proteins with a series of vectors. We then trained and optimized our model using random forest (RF), extreme gradient boosting, light gradient boosting machine, and logistic regression models. RESULTS Comprehensive experiments demonstrated the good performance of our proposed method with an area under the receiver operating characteristic curve (AUC) value between 0.72 and 0.76. Our model also outperformed baseline methods for overall disease protein prediction, including the random walk with restart, average commute time, and Katz models. Compared with the PPI network constructed from the baseline models, complementation of keywords in the PPIK network improved the performance (AUC) by 0.08 on average, and the metagraph-based method improved the AUC by 0.30 on average compared with that of the baseline methods. According to the comprehensive performance of the four models, RF was selected as the best model for disease protein prediction, with precision, recall, F1-score, and AUC values of 0.76, 0.73, 0.72, and 0.76, respectively. We transformed these proteins to their encoding gene IDs and identified the top 20 genes as the most probable schizophrenia-risk genes, including the EYA3, CNTN4, HSPA8, LRRK2, and AFP genes. We further validated these outcomes against metagraph features and evidence from the literature, performed a features analysis, and exploited evidence from the literature to interpret the correlation between the predicted genes and diseases. CONCLUSIONS The metagraph representation based on the PPIK network framework was found to be effective for potential schizophrenia risk genes identification. The results are quite reliable as evidence can be found in the literature to support our prediction. Our approach can provide more biological insights into the pathogenesis of schizophrenia.
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Affiliation(s)
- Shirui Yu
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
| | - Ziyang Wang
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
| | - Jiale Nan
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
| | - Aihua Li
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
| | - Xuemei Yang
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaoli Tang
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
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Casotti MC, Meira DD, Alves LNR, Bessa BGDO, Campanharo CV, Vicente CR, Aguiar CC, Duque DDA, Barbosa DG, dos Santos EDVW, Garcia FM, de Paula F, Santana GM, Pavan IP, Louro LS, Braga RFR, Trabach RSDR, Louro TS, de Carvalho EF, Louro ID. Translational Bioinformatics Applied to the Study of Complex Diseases. Genes (Basel) 2023; 14:419. [PMID: 36833346 PMCID: PMC9956936 DOI: 10.3390/genes14020419] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/29/2023] [Accepted: 01/31/2023] [Indexed: 02/10/2023] Open
Abstract
Translational Bioinformatics (TBI) is defined as the union of translational medicine and bioinformatics. It emerges as a major advance in science and technology by covering everything, from the most basic database discoveries, to the development of algorithms for molecular and cellular analysis, as well as their clinical applications. This technology makes it possible to access the knowledge of scientific evidence and apply it to clinical practice. This manuscript aims to highlight the role of TBI in the study of complex diseases, as well as its application to the understanding and treatment of cancer. An integrative literature review was carried out, obtaining articles through several websites, among them: PUBMED, Science Direct, NCBI-PMC, Scientific Electronic Library Online (SciELO), and Google Academic, published in English, Spanish, and Portuguese, indexed in the referred databases and answering the following guiding question: "How does TBI provide a scientific understanding of complex diseases?" An additional effort is aimed at the dissemination, inclusion, and perpetuation of TBI knowledge from the academic environment to society, helping the study, understanding, and elucidating of complex disease mechanics and their treatment.
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Affiliation(s)
- Matheus Correia Casotti
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Débora Dummer Meira
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Lyvia Neves Rebello Alves
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | | | - Camilly Victória Campanharo
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Creuza Rachel Vicente
- Departamento de Medicina Social, Universidade Federal do Espírito Santo, Vitória 29040-090, Espírito Santo, Brazil
| | - Carla Carvalho Aguiar
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Daniel de Almeida Duque
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Débora Gonçalves Barbosa
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | | | - Fernanda Mariano Garcia
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Flávia de Paula
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Gabriel Mendonça Santana
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Isabele Pagani Pavan
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Luana Santos Louro
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Raquel Furlani Rocon Braga
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Raquel Silva dos Reis Trabach
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Thomas Santos Louro
- Escola Superior de Ciências da Santa Casa de Misericórdia de Vitória (EMESCAM), Vitória 29027-502, Espírito Santo, Brazil
| | - Elizeu Fagundes de Carvalho
- Instituto de Biologia Roberto Alcantara Gomes (IBRAG), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro 20551-030, Rio de Janeiro, Brazil
| | - Iúri Drumond Louro
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
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Voitalov I, Zhang L, Kilpatrick C, Withers JB, Saleh A, Akmaev VR, Ghiassian SD. The module triad: a novel network biology approach to utilize patients' multi-omics data for target discovery in ulcerative colitis. Sci Rep 2022; 12:21685. [PMID: 36522454 PMCID: PMC9755270 DOI: 10.1038/s41598-022-26276-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Tumor necrosis factor-[Formula: see text] inhibitors (TNFi) have been a standard treatment in ulcerative colitis (UC) for nearly 20 years. However, insufficient response rate to TNFi therapies along with concerns around their immunogenicity and inconvenience of drug delivery through injections calls for development of UC drugs targeting alternative proteins. Here, we propose a multi-omic network biology method for prioritization of protein targets for UC treatment. Our method identifies network modules on the Human Interactome-a network of protein-protein interactions in human cells-consisting of genes contributing to the predisposition to UC (Genotype module), genes whose expression needs to be modulated to achieve low disease activity (Response module), and proteins whose perturbation alters expression of the Response module genes to a healthy state (Treatment module). Targets are prioritized based on their topological relevance to the Genotype module and functional similarity to the Treatment module. We demonstrate utility of our method in UC and other complex diseases by efficiently recovering the protein targets associated with compounds in clinical trials and on the market . The proposed method may help to reduce cost and time of drug development by offering a computational screening tool for identification of novel and repurposing therapeutic opportunities in UC and other complex diseases.
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Affiliation(s)
- Ivan Voitalov
- Scipher Medicine Corporation, 221 Crescent St Suite 103A, Waltham, MA 02453 USA
| | - Lixia Zhang
- Scipher Medicine Corporation, 221 Crescent St Suite 103A, Waltham, MA 02453 USA
| | - Casey Kilpatrick
- Scipher Medicine Corporation, 221 Crescent St Suite 103A, Waltham, MA 02453 USA
| | - Johanna B. Withers
- Scipher Medicine Corporation, 221 Crescent St Suite 103A, Waltham, MA 02453 USA
| | - Alif Saleh
- Scipher Medicine Corporation, 221 Crescent St Suite 103A, Waltham, MA 02453 USA
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Qumsiyeh E, Showe L, Yousef M. GediNET for discovering gene associations across diseases using knowledge based machine learning approach. Sci Rep 2022; 12:19955. [PMID: 36402891 PMCID: PMC9675776 DOI: 10.1038/s41598-022-24421-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 11/15/2022] [Indexed: 11/21/2022] Open
Abstract
The most common approaches to discovering genes associated with specific diseases are based on machine learning and use a variety of feature selection techniques to identify significant genes that can serve as biomarkers for a given disease. More recently, the integration in this process of prior knowledge-based approaches has shown significant promise in the discovery of new biomarkers with potential translational applications. In this study, we developed a novel approach, GediNET, that integrates prior biological knowledge to gene Groups that are shown to be associated with a specific disease such as a cancer. The novelty of GediNET is that it then also allows the discovery of significant associations between that specific disease and other diseases. The initial step in this process involves the identification of gene Groups. The Groups are then subjected to a Scoring component to identify the top performing classification Groups. The top-ranked gene Groups are then used to train a Machine Learning Model. The process of Grouping, Scoring and Modelling (G-S-M) is used by GediNET to identify other diseases that are similarly associated with this signature. GediNET identifies these relationships through Disease-Disease Association (DDA) based machine learning. DDA explores novel associations between diseases and identifies relationships which could be used to further improve approaches to diagnosis, prognosis, and treatment. The GediNET KNIME workflow can be downloaded from: https://github.com/malikyousef/GediNET.git or https://kni.me/w/3kH1SQV_mMUsMTS .
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Affiliation(s)
- Emma Qumsiyeh
- Information Technology Engineering, Al-Quds University, Abu Dis, Palestine.
| | - Louise Showe
- The Wistar Institute, Philadelphia, PA, 19104, USA
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, 13206, Zefat, Israel.
- Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel.
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Xiang J, Meng X, Zhao Y, Wu FX, Li M. HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure. Brief Bioinform 2022; 23:6547263. [PMID: 35275996 DOI: 10.1093/bib/bbac072] [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: 10/20/2021] [Revised: 01/18/2022] [Accepted: 02/13/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. RESULTS We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. CONCLUSIONS The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.
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Affiliation(s)
- Ju Xiang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China; Department of Basic Medical Sciences & Academician Workstation, Changsha Medical University, Changsha, Hunan 410219, China
| | - Xiangmao Meng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Wang L, Wu M, Wu Y, Zhang X, Li S, He M, Zhang F, Wang Y, Li J. Prediction of the Disease Causal Genes Based on Heterogeneous Network and Multi-Feature Combination Method. Comput Biol Chem 2022; 97:107639. [DOI: 10.1016/j.compbiolchem.2022.107639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 01/05/2022] [Accepted: 02/07/2022] [Indexed: 11/30/2022]
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Zhang H, Ferguson A, Robertson G, Jiang M, Zhang T, Sudlow C, Smith K, Rannikmae K, Wu H. Benchmarking network-based gene prioritization methods for cerebral small vessel disease. Brief Bioinform 2021; 22:bbab006. [PMID: 33634312 PMCID: PMC8425308 DOI: 10.1093/bib/bbab006] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 12/31/2020] [Accepted: 01/04/2021] [Indexed: 12/25/2022] Open
Abstract
Network-based gene prioritization algorithms are designed to prioritize disease-associated genes based on known ones using biological networks of protein interactions, gene-disease associations (GDAs) and other relationships between biological entities. Various algorithms have been developed based on different mechanisms, but it is not obvious which algorithm is optimal for a specific disease. To address this issue, we benchmarked multiple algorithms for their application in cerebral small vessel disease (cSVD). We curated protein-gene interactions (PGIs) and GDAs from databases and assembled PGI networks and disease-gene heterogeneous networks. A screening of algorithms resulted in seven representative algorithms to be benchmarked. Performance of algorithms was assessed using both leave-one-out cross-validation (LOOCV) and external validation with MEGASTROKE genome-wide association study (GWAS). We found that random walk with restart on the heterogeneous network (RWRH) showed best LOOCV performance, with median LOOCV rediscovery rank of 185.5 (out of 19 463 genes). The GenePanda algorithm had most GWAS-confirmable genes in top 200 predictions, while RWRH had best ranks for small vessel stroke-associated genes confirmed in GWAS. In conclusion, RWRH has overall better performance for application in cSVD despite its susceptibility to bias caused by degree centrality. Choice of algorithms should be determined before applying to specific disease. Current pure network-based gene prioritization algorithms are unlikely to find novel disease-associated genes that are not associated with known ones. The tools for implementing and benchmarking algorithms have been made available and can be generalized for other diseases.
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Affiliation(s)
- Huayu Zhang
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Amy Ferguson
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Grant Robertson
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Muchen Jiang
- Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Teng Zhang
- Department of Orthopaedics and Traumatology, the University of Hong Kong, Hong Kong, China
| | - Cathie Sudlow
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Keith Smith
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Kristiina Rannikmae
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Honghan Wu
- Health Data Research UK, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
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Xiang J, Zhang J, Zheng R, Li X, Li M. NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction. Brief Bioinform 2021; 22:6236070. [PMID: 33866352 DOI: 10.1093/bib/bbab080] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/11/2021] [Accepted: 02/21/2021] [Indexed: 12/12/2022] Open
Abstract
The prediction of genes related to diseases is important to the study of the diseases due to high cost and time consumption of biological experiments. Network propagation is a popular strategy for disease-gene prediction. However, existing methods focus on the stable solution of dynamics while ignoring the useful information hidden in the dynamical process, and it is still a challenge to make use of multiple types of physical/functional relationships between proteins/genes to effectively predict disease-related genes. Therefore, we proposed a framework of network impulsive dynamics on multiplex biological network (NIDM) to predict disease-related genes, along with four variants of NIDM models and four kinds of impulsive dynamical signatures (IDSs). NIDM is to identify disease-related genes by mining the dynamical responses of nodes to impulsive signals being exerted at specific nodes. By a series of experimental evaluations in various types of biological networks, we confirmed the advantage of multiplex network and the important roles of functional associations in disease-gene prediction, demonstrated superior performance of NIDM compared with four types of network-based algorithms and then gave the effective recommendations of NIDM models and IDS signatures. To facilitate the prioritization and analysis of (candidate) genes associated to specific diseases, we developed a user-friendly web server, which provides three kinds of filtering patterns for genes, network visualization, enrichment analysis and a wealth of external links (http://bioinformatics.csu.edu.cn/DGP/NID.jsp). NIDM is a protocol for disease-gene prediction integrating different types of biological networks, which may become a very useful computational tool for the study of disease-related genes.
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Affiliation(s)
- Ju Xiang
- School of Computer Science and Engineering, Central South University, Human, China
| | - Jiashuai Zhang
- School of Computer Science and Engineering, Central South University, Human, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, China
| | - Xingyi Li
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, China
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Ata SK, Wu M, Fang Y, Ou-Yang L, Kwoh CK, Li XL. Recent advances in network-based methods for disease gene prediction. Brief Bioinform 2020; 22:6023077. [PMID: 33276376 DOI: 10.1093/bib/bbaa303] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/29/2020] [Accepted: 10/10/2020] [Indexed: 01/28/2023] Open
Abstract
Disease-gene association through genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms that correlate with specific diseases needs statistical analysis of associations. Considering the huge number of possible mutations, in addition to its high cost, another important drawback of GWAS analysis is the large number of false positives. Thus, researchers search for more evidence to cross-check their results through different sources. To provide the researchers with alternative and complementary low-cost disease-gene association evidence, computational approaches come into play. Since molecular networks are able to capture complex interplay among molecules in diseases, they become one of the most extensively used data for disease-gene association prediction. In this survey, we aim to provide a comprehensive and up-to-date review of network-based methods for disease gene prediction. We also conduct an empirical analysis on 14 state-of-the-art methods. To summarize, we first elucidate the task definition for disease gene prediction. Secondly, we categorize existing network-based efforts into network diffusion methods, traditional machine learning methods with handcrafted graph features and graph representation learning methods. Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases. We also provide distinguishing findings about the discussed methods based on our empirical analysis. Finally, we highlight potential research directions for future studies on disease gene prediction.
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Affiliation(s)
- Sezin Kircali Ata
- School of Computer Science and Engineering Nanyang Technological University (NTU)
| | - Min Wu
- Institute for Infocomm Research (I2R), A*STAR, Singapore
| | - Yuan Fang
- School of Information Systems, Singapore Management University, Singapore
| | - Le Ou-Yang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen China
| | | | - Xiao-Li Li
- Department head and principal scientist at I2R, A*STAR, Singapore
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Fernando PC, Mabee PM, Zeng E. Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities. BMC Bioinformatics 2020; 21:442. [PMID: 33028186 PMCID: PMC7542696 DOI: 10.1186/s12859-020-03773-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 09/22/2020] [Indexed: 01/04/2023] Open
Abstract
Background Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein–protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. Moreover, PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI networks by reducing the number of false positive and false negative interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks were semantic networks, as they were constructed based on the anatomy ontology annotations that were obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database and compared the performance of their network-based candidate gene predictions. Results According to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks, which were semantically improved PPI networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse. Conclusion Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities.
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Affiliation(s)
- Pasan C Fernando
- Department of Biology, University of South Dakota, Vermillion, SD, USA.
| | - Paula M Mabee
- Department of Biology, University of South Dakota, Vermillion, SD, USA.,National Ecological Observatory Network, Battelle Memorial Institute, 1685 38th St., Suite 100, Boulder, CO, 80301, USA
| | - Erliang Zeng
- Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA, USA. .,Department of Preventive and Community Dentistry, College of Dentistry, University of Iowa, Iowa City, IA, USA. .,Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA. .,Department of Biomedical Engineering, College of Engineering, University of Iowa, Iowa City, IA, USA.
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12
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Bean DM, Al-Chalabi A, Dobson RJB, Iacoangeli A. A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis. Genes (Basel) 2020; 11:E668. [PMID: 32575372 PMCID: PMC7349022 DOI: 10.3390/genes11060668] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 06/13/2020] [Accepted: 06/16/2020] [Indexed: 02/07/2023] Open
Abstract
Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two to five years of first symptoms. Several rare disruptive gene variants have been associated with ALS and are responsible for about 15% of all cases. Although our knowledge of the genetic landscape of this disease is improving, it remains limited. Machine learning models trained on the available protein-protein interaction and phenotype-genotype association data can use our current knowledge of the disease genetics for the prediction of novel candidate genes. Here, we describe a knowledge-based machine learning method for this purpose. We trained our model on protein-protein interaction data from IntAct, gene function annotation from Gene Ontology, and known disease-gene associations from DisGeNet. Using several sets of known ALS genes from public databases and a manual review as input, we generated a list of new candidate genes for each input set. We investigated the relevance of the predicted genes in ALS by using the available summary statistics from the largest ALS genome-wide association study and by performing functional and phenotype enrichment analysis. The predicted sets were enriched for genes associated with other neurodegenerative diseases known to overlap with ALS genetically and phenotypically, as well as for biological processes associated with the disease. Moreover, using ALS genes from ClinVar and our manual review as input, the predicted sets were enriched for ALS-associated genes (ClinVar p = 0.038 and manual review p = 0.060) when used for gene prioritisation in a genome-wide association study.
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Affiliation(s)
- Daniel M. Bean
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Health Data Research UK London, University College London, 16 De Crespigny Park, London SE5 8AF, UK
| | - Ammar Al-Chalabi
- King′s College Hospital, Bessemer Road, Denmark Hill, Brixton, London SE5 9RS, UK;
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King′s College London, London, 5 Cutcombe Rd, Brixton, London SE5 9RT, UK
| | - Richard J. B. Dobson
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Health Data Research UK London, University College London, 16 De Crespigny Park, London SE5 8AF, UK
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, UK
| | - Alfredo Iacoangeli
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King′s College London, London, 5 Cutcombe Rd, Brixton, London SE5 9RT, UK
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13
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Cheng L, Wang P, Tian R, Wang S, Guo Q, Luo M, Zhou W, Liu G, Jiang H, Jiang Q. LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Res 2020; 47:D140-D144. [PMID: 30380072 PMCID: PMC6323902 DOI: 10.1093/nar/gky1051] [Citation(s) in RCA: 231] [Impact Index Per Article: 57.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 10/26/2018] [Indexed: 12/12/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) play crucial roles in regulating gene expression, and a growing number of researchers have focused on the identification of target genes of lncRNAs. However, no online repository is available to collect the information on target genes regulated by lncRNAs. To make it convenient for researchers to know what genes are regulated by a lncRNA of interest, we developed a database named lncRNA2Target to provide a comprehensive resource of lncRNA target genes in 2015. To update the database this year, we retrieved all new lncRNA-target relationships from papers published from 1 August 2014 to 30 April 2018 and RNA-seq datasets before and after knockdown or overexpression of a specific lncRNA. LncRNA2Target database v2.0 provides a web interface through which its users can search for the targets of a particular lncRNA or for the lncRNAs that target a particular gene, and is freely accessible at http://123.59.132.21/lncrna2target.
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Affiliation(s)
- Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Pingping Wang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Rui Tian
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Song Wang
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
| | - Qinghua Guo
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Meng Luo
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Wenyang Zhou
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Guiyou Liu
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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14
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Peng J, Zhu L, Wang Y, Chen J. Mining Relationships among Multiple Entities in Biological Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:769-776. [PMID: 30872239 DOI: 10.1109/tcbb.2019.2904965] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Identifying topological relationships among multiple entities in biological networks is critical towards the understanding of the organizational principles of network functionality. Theoretically, this problem can be solved using minimum Steiner tree (MSTT) algorithms. However, due to large network size, it remains to be computationally challenging, and the predictive value of multi-entity topological relationships is still unclear. We present a novel solution called Cluster-based Steiner Tree Miner (CST-Miner) to instantly identify multi-entity topological relationships in biological networks. Given a list of user-specific entities, CST-Miner decomposes a biological network into nested cluster-based subgraphs, on which multiple minimum Steiner trees are identified. By merging all of them into a minimum cost tree, the optimal topological relationships among all the user-specific entities are revealed. Experimental results showed that CST-Miner can finish in nearly log-linear time and the tree constructed by CST-Miner is close to the global minimum.
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15
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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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16
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Peng J, Guan J, Shang X. Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder. Front Genet 2019; 10:226. [PMID: 31001311 PMCID: PMC6454041 DOI: 10.3389/fgene.2019.00226] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 02/28/2019] [Indexed: 12/26/2022] Open
Abstract
Identifying genes associated with Parkinson's disease plays an extremely important role in the diagnosis and treatment of Parkinson's disease. In recent years, based on the guilt-by-association hypothesis, many methods have been proposed to predict disease-related genes, but few of these methods are designed or used for Parkinson's disease gene prediction. In this paper, we propose a novel prediction method for Parkinson's disease gene prediction, named N2A-SVM. N2A-SVM includes three parts: extracting features of genes based on network, reducing the dimension using deep neural network, and predicting Parkinson's disease genes using a machine learning method. The evaluation test shows that N2A-SVM performs better than existing methods. Furthermore, we evaluate the significance of each step in the N2A-SVM algorithm and the influence of the hyper-parameters on the result. In addition, we train N2A-SVM on the recent dataset and used it to predict Parkinson's disease genes. The predicted top-rank genes can be verified based on literature study.
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Affiliation(s)
| | | | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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17
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Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology. PLoS One 2018; 13:e0208626. [PMID: 30532199 PMCID: PMC6287949 DOI: 10.1371/journal.pone.0208626] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Accepted: 11/20/2018] [Indexed: 12/14/2022] Open
Abstract
Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD). We also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at https://github.com/Muh-Asif/ASD-genes-prediction.
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18
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Abstract
BACKGROUND Recently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between phenotype-associated proteins, which may lead to inaccurate phenotype similarity. RESULTS We proposed a network-based method PhenoNet to calculate the similarity between phenotypes. We localized phenotypes in the network and calculated the similarity between phenotype-associated modules by modeling both the inter- and intra-similarity. CONCLUSIONS PhenoNet was evaluated on two independent evaluation datasets: gene ontology and gene expression data. The result shows that PhenoNet performs better than the state-of-art methods on all evaluation tests.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Weiwei Hui
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
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19
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Peng J, Zhang X, Hui W, Lu J, Li Q, Liu S, Shang X. Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach. BMC SYSTEMS BIOLOGY 2018; 12:18. [PMID: 29560823 PMCID: PMC5861498 DOI: 10.1186/s12918-018-0539-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and gene annotations. RESULTS We present NETSIM2, a new network-based method that allows researchers to measure GO-based gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based gene functional similarity. CONCLUSIONS Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global gene-to-gene interactions in the co-functional network, especially on the species that gene annotations in GO are far from complete.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China. .,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, China. .,Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
| | - Xuanshuo Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Weiwei Hui
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Junya Lu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Qianqian Li
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Shuhui Liu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, China
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20
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Peng J, Wang H, Lu J, Hui W, Wang Y, Shang X. Identifying term relations cross different gene ontology categories. BMC Bioinformatics 2017; 18:573. [PMID: 29297309 PMCID: PMC5751813 DOI: 10.1186/s12859-017-1959-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Background The Gene Ontology (GO) is a community-based bioinformatics resource that employs ontologies to represent biological knowledge and describes information about gene and gene product function. GO includes three independent categories: molecular function, biological process and cellular component. For better biological reasoning, identifying the biological relationships between terms in different categories are important. However, the existing measurements to calculate similarity between terms in different categories are either developed by using the GO data only or only take part of combined gene co-function network information. Results We propose an iterative ranking-based method called CroGO2 to measure the cross-categories GO term similarities by incorporating level information of GO terms with both direct and indirect interactions in the gene co-function network. Conclusions The evaluation test shows that CroGO2 performs better than the existing methods. A genome-specific term association network for yeast is also generated by connecting terms with the high confidence score. The linkages in the term association network could be supported by the literature. Given a gene set, the related terms identified by using the association network have overlap with the related terms identified by GO enrichment analysis.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Honggang Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Junya Lu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Weiwei Hui
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
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21
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Yang F, Wu D, Lin L, Yang J, Yang T, Zhao J. The integration of weighted gene association networks based on information entropy. PLoS One 2017; 12:e0190029. [PMID: 29272314 PMCID: PMC5741255 DOI: 10.1371/journal.pone.0190029] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 12/06/2017] [Indexed: 01/18/2023] Open
Abstract
Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort.
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Affiliation(s)
- Fan Yang
- Department of Mathematics, Army Logistics University of PLA, Chongqing, China
| | - Duzhi Wu
- Rongzhi College of Chongqing Technology and Business, Chongqing, China
- * E-mail: (DW); (JZ)
| | - Limei Lin
- Department of Mathematics, Army Logistics University of PLA, Chongqing, China
| | - Jian Yang
- School of Pharmacy, Second Military Medical University, Shanghai, China
| | - Tinghong Yang
- Department of Mathematics, Army Logistics University of PLA, Chongqing, China
| | - Jing Zhao
- Institute of Interdisciplinary Complex Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- * E-mail: (DW); (JZ)
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22
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Disease gene classification with metagraph representations. Methods 2017; 131:83-92. [DOI: 10.1016/j.ymeth.2017.06.036] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 06/23/2017] [Accepted: 06/30/2017] [Indexed: 12/28/2022] Open
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Hu Y, Zhao L, Liu Z, Ju H, Shi H, Xu P, Wang Y, Cheng L. DisSetSim: an online system for calculating similarity between disease sets. J Biomed Semantics 2017; 8:28. [PMID: 29297411 PMCID: PMC5763469 DOI: 10.1186/s13326-017-0140-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Background Functional similarity between molecules results in similar phenotypes, such as diseases. Therefore, it is an effective way to reveal the function of molecules based on their induced diseases. However, the lack of a tool for obtaining the similarity score of pair-wise disease sets (SSDS) limits this type of application. Results Here, we introduce DisSetSim, an online system to solve this problem in this article. Five state-of-the-art methods involving Resnik’s, Lin’s, Wang’s, PSB, and SemFunSim methods were implemented to measure the similarity score of pair-wise diseases (SSD) first. And then “pair-wise-best pairs-average” (PWBPA) method was implemented to calculated the SSDS by the SSD. The system was applied for calculating the functional similarity of miRNAs based on their induced disease sets. The results were further used to predict potential disease-miRNA relationships. Conclusions The high area under the receiver operating characteristic curve AUC (0.9296) based on leave-one-out cross validation shows that the PWBPA method achieves a high true positive rate and a low false positive rate. The system can be accessed from http://www.bio-annotation.cn:8080/DisSetSim/.
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Affiliation(s)
- Yang Hu
- Harbin Institute of Technology, School of Life Science and Technology, Harbin, 150001, People's Republic of China
| | - Lingling Zhao
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, People's Republic of China
| | - Zhiyan Liu
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, People's Republic of China
| | - Hong Ju
- Department of information engineering, Heilongjiang Biological Science and Technology Career Academy, Harbin, 150001, People's Republic of China
| | - Hongbo Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150001, People's Republic of China
| | - Peigang Xu
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, People's Republic of China
| | - Yadong Wang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150001, People's Republic of China.
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24
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Zhang C, Li X, Li S, Feng Z. Dynamically analyzing cell interactions in biological environments using multiagent social learning framework. J Biomed Semantics 2017; 8:31. [PMID: 29297360 PMCID: PMC5763467 DOI: 10.1186/s13326-017-0142-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biology and are of great significance for the study of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent's behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging. RESULTS In this work, we present a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), and model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. We experimentally verify the predictive power of our model using a number of representative games. Experimental results confirm the theoretical analysis. CONCLUSION Under multiagent social learning framework, we modeled the behavior of agent in biologic environment, and theoretically analyzed the dynamics of the model. We present some sufficient conditions about convergence or non-convergence and prove them theoretically. It can be used to predict the convergence of the system.
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Affiliation(s)
- Chengwei Zhang
- School of Computer Science and Technology, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Xiaohong Li
- School of Computer Science and Technology, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Shuxin Li
- School of Computer Science and Technology, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Zhiyong Feng
- School of Computer Computer Software, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
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Teng Z, Guo M, Liu X, Tian Z, Che K. Revealing protein functions based on relationships of interacting proteins and GO terms. J Biomed Semantics 2017; 8:27. [PMID: 29297388 PMCID: PMC5763294 DOI: 10.1186/s13326-017-0139-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND In recent years, numerous computational methods predicted protein function based on the protein-protein interaction (PPI) network. These methods supposed that two proteins share the same function if they interact with each other. However, it is reported by recent studies that the functions of two interacting proteins may be just related. It will mislead the prediction of protein function. Therefore, there is a need for investigating the functional relationship between interacting proteins. RESULTS In this paper, the functional relationship between interacting proteins is studied and a novel method, called as GoDIN, is advanced to annotate functions of interacting proteins in Gene Ontology (GO) context. It is assumed that the functional difference between interacting proteins can be expressed by semantic difference between GO term and its relatives. Thus, the method uses GO term and its relatives to annotate the interacting proteins separately according to their functional roles in the PPI network. The method is validated by a series of experiments and compared with the concerned method. The experimental results confirm the assumption and suggest that GoDIN is effective on predicting functions of protein. CONCLUSIONS This study demonstrates that: (1) interacting proteins are not equal in the PPI network, and their function may be same or similar, or just related; (2) functional difference between interacting proteins can be measured by their degrees in the PPI network; (3) functional relationship between interacting proteins can be expressed by relationship between GO term and its relatives.
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Affiliation(s)
- Zhixia Teng
- Department of Information Management and Information System, Northeast Forestry University, Harbin, 150040, China.
- Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
| | - Maozu Guo
- Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
| | - Xiaoyan Liu
- Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Zhen Tian
- Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Kai Che
- Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
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Abstract
Background More than 1/3 of human genes are regulated by microRNAs. The identification of microRNA (miRNA) is the precondition of discovering the regulatory mechanism of miRNA and developing the cure for genetic diseases. The traditional identification method is biological experiment, but it has the defects of long period, high cost, and missing the miRNAs that but also many other algorithms only exist in a specific period or low expression level. Therefore, to overcome these defects, machine learning method is applied to identify miRNAs. Results In this study, for identifying real and pseudo miRNAs and classifying different species, we extracted 98 dimensional features based on the primary and secondary structure, then we proposed the BP-Adaboost method to figure out the overfitting phenomenon of BP neural network by constructing multiple BP neural network classifiers and distributed weights to these classifiers. The novel method we proposed, from the 4 evaluation terms, have achieved greatly improvement on the effect of identifying true pre-RNA compared to other methods. And from the respect of identifying species of pre-RNA, the novel method achieved more accuracy than other algorithms. Conclusions The BP-Adaboost method has achieved more than 98% accuracy in identifying real and pseudo miRNAs. It is much higher than not only BP but also many other algorithms. In the second experiment, restricted by the data, the algorithm could not get high accuracy in identifying 7 species, but also better than other algorithms.
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Peng J, Li Q, Shang X. Investigations on factors influencing HPO-based semantic similarity calculation. J Biomed Semantics 2017; 8:34. [PMID: 29297376 PMCID: PMC5763495 DOI: 10.1186/s13326-017-0144-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Background Although disease diagnosis has greatly benefited from next generation sequencing technologies, it is still difficult to make the right diagnosis purely based on sequencing technologies for many diseases with complex phenotypes and high genetic heterogeneity. Recently, calculating Human Phenotype Ontology (HPO)-based phenotype semantic similarity has contributed a lot for completing disease diagnosis. However, factors which affect the accuracy of HPO-based semantic similarity have not been evaluated systematically. Results In this study, we proposed a new framework called HPOFactor to evaluate these factors. Our model includes four components: (1) the size of annotation set, (2) the evidence code of annotations, (3) the quality of annotations and (4) the coverage of annotations respectively. Conclusions HPOFactor analyzes the four factors systematically based on two kinds of experiments: causative gene prediction and disease prediction. Furthermore, semantic similarity measurement could be designed based on the characteristic of these factors.
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Affiliation(s)
- Jiajie Peng
- Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Qianqian Li
- Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Xuequn Shang
- Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China.
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Dongliang X, Jingchang P, Bailing W. Multiple kernels learning-based biological entity relationship extraction method. J Biomed Semantics 2017; 8:38. [PMID: 29297359 PMCID: PMC5763518 DOI: 10.1186/s13326-017-0138-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques. Results The P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2–5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel. Conclusion In comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.
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Affiliation(s)
- Xu Dongliang
- School of Mechanical, Electrical and Information Engineering, ShanDong University, WenHua West Road, WeiHai, 264209, China
| | - Pan Jingchang
- School of Mechanical, Electrical and Information Engineering, ShanDong University, WenHua West Road, WeiHai, 264209, China.
| | - Wang Bailing
- School of Computer Science and Technology, Harbin Institute of Technology, WenHua West Road, WeiHai, 264209, China
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Tian Z, Guo M, Wang C, Xing L, Wang L, Zhang Y. Constructing an integrated gene similarity network for the identification of disease genes. J Biomed Semantics 2017; 8:32. [PMID: 29297379 PMCID: PMC5763299 DOI: 10.1186/s13326-017-0141-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. RESULTS We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. CONCLUSIONS RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .
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Affiliation(s)
- Zhen Tian
- School of Computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001 People’s Republic of China
| | - Maozu Guo
- School of Computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001 People’s Republic of China
| | - Chunyu Wang
- School of Computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001 People’s Republic of China
| | - LinLin Xing
- School of Computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001 People’s Republic of China
| | - Lei Wang
- Institute of Health Service and Medical Information Academy of Military Medical Sciences Beijing, Beijing, 100850 China
| | - Yin Zhang
- Institute of Health Service and Medical Information Academy of Military Medical Sciences Beijing, Beijing, 100850 China
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