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Wang KX, Gao Y, Gong WX, Ye XF, Fan LY, Wang C, Gao XF, Gao L, Du GH, Qin XM, Lu AP, Guan DG. A Novel Strategy for Decoding and Validating the Combination Principles of Huanglian Jiedu Decoction From Multi-Scale Perspective. Front Pharmacol 2020; 11:567088. [PMID: 33424585 PMCID: PMC7789881 DOI: 10.3389/fphar.2020.567088] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 11/12/2020] [Indexed: 12/12/2022] Open
Abstract
Traditional Chinese medicine (TCM) formulas treat complex diseases through combined botanical drugs which follow specific compatibility rules to reduce toxicity and increase efficiency. "Jun, Chen, Zuo and Shi" is one of most used compatibility rules in the combination of botanical drugs. However, due to the deficiency of traditional research methods, the quantified theoretical basis of herbal compatibility including principles of "Jun, Chen, Zuo and Shi" are still unclear. Network pharmacology is a new strategy based on system biology and multi-disciplines, which can systematically and comprehensively observe the intervention of drugs on disease networks, and is especially suitable for the research of TCM in the treatment of complex diseases. In this study, we systematically decoded the "Jun, Chen, Zuo and Shi" rules of Huanglian Jiedu Decoction (HJD) in the treatment of diseases for the first time. This interpretation method considered three levels of data. The data in the first level mainly depicts the characteristics of each component in single botanical drug of HJD, include the physical and chemical properties of component, ADME properties and functional enrichment analysis of component targets. The second level data is the characterization of component-target-protein (C-T-P) network in the whole protein-protein interaction (PPI) network, mainly include the characterization of degree and key communities in C-T-P network. The third level data is the characterization of intervention propagation properties of HJD in the treatment of different complex diseases, mainly include target coverage of pathogenic genes and propagation coefficient of intervention effect between target proteins and pathogenic genes. Finally, our method was validated by metabolic data, which could be used to detect the components absorbed into blood. This research shows the scientific basis of "Jun-Chen-Zuo-Shi" from a multi-dimensional perspective, and provides a good methodological reference for the subsequent interpretation of key components and speculation mechanism of the formula.
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Affiliation(s)
- Ke-Xin Wang
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China.,Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China
| | - Yao Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China.,Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China
| | - Wen-Xia Gong
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Xiao-Feng Ye
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China
| | - Liu-Yi Fan
- Department of Orthopaedics and Traumatology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Chun Wang
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xue-Fei Gao
- Department of Physiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Li Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Guan-Hua Du
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China.,Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xue-Mei Qin
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Ai-Ping Lu
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China
| | - Dao-Gang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Guangdong Key Laboratory of Biochip Technology, Southern Medical University, Guangzhou, China
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Abstract
Background:
Revealing the subcellular location of a newly discovered protein can
bring insight into their function and guide research at the cellular level. The experimental methods
currently used to identify the protein subcellular locations are both time-consuming and expensive.
Thus, it is highly desired to develop computational methods for efficiently and effectively identifying
the protein subcellular locations. Especially, the rapidly increasing number of protein sequences
entering the genome databases has called for the development of automated analysis methods.
Methods:
In this review, we will describe the recent advances in predicting the protein subcellular
locations with machine learning from the following aspects: i) Protein subcellular location benchmark
dataset construction, ii) Protein feature representation and feature descriptors, iii) Common
machine learning algorithms, iv) Cross-validation test methods and assessment metrics, v) Web
servers.
Result & Conclusion:
Concomitant with a large number of protein sequences generated by highthroughput
technologies, four future directions for predicting protein subcellular locations with
machine learning should be paid attention. One direction is the selection of novel and effective features
(e.g., statistics, physical-chemical, evolutional) from the sequences and structures of proteins.
Another is the feature fusion strategy. The third is the design of a powerful predictor and the fourth
one is the protein multiple location sites prediction.
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Affiliation(s)
- Ting-He Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Shao-Wu Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
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Biswas A, Raza A, Das S, Kapoor M, Jayarajan R, Verma A, Shamsudheen KV, Murry B, Seth S, Bhargava B, Scaria V, Sivasubbu S, Rao VR. Loss of function mutation in the P2X7, a ligand-gated ion channel gene associated with hypertrophic cardiomyopathy. Purinergic Signal 2019; 15:205-210. [PMID: 31152337 DOI: 10.1007/s11302-019-09660-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Accepted: 05/16/2019] [Indexed: 12/21/2022] Open
Abstract
Hypertrophic cardiomyopathy (HCM) is an inherited heart failure condition, mostly found to have genetic abnormalities, and is a leading cause of sudden death in young adults. Whole exome sequencing should be given consideration as a molecular diagnostic tool to identify disease-causing mutation/s. In this study, a HCM family with multiple affected members having history of sudden death were subjected to exome sequencing along with unaffected members. Quality passed variants obtained were filtered for rarity (MAF > 0.5%), evolutionary conservation, pathogenic prediction, and segregation in affected members after removing shared variants present in unaffected members. Only one non-synonymous mutation (p. Glu186Lys or E186K) in exon 6 of P2X7 gene segregated in HCM-affected individuals which was absent in unaffected family members and 100 clinically evaluated controls. The site of the mutation is highly conserved and led to complete loss of function which is in close vicinity to ATP-binding site-forming residues, affecting ATP binding, channel gating, or both. Mutations in candidate genes which were not segregated define clinical heterogeneity within affected members. P2X7 gene is highly expressed in the heart and shows direct interaction with major candidate genes for HCM. Our results reveal a significant putative HCM causative gene, P2X7, for the first time and show that germ-line mutations in P2X7 may cause a defective phenotype, suggesting purinergic receptor involvement in heart failure mediated through arrhythmias which need further investigations to be targeted for therapeutic interventions.
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Affiliation(s)
- Amitabh Biswas
- Department of Anthropology, University of Delhi, New Delhi, India
- College of Natural Sciences, Arba Minch University, Arba Minch, Ethiopia
| | - Ali Raza
- College of Natural Sciences, Arba Minch University, Arba Minch, Ethiopia
| | - Soumi Das
- Department of Anthropology, University of Delhi, New Delhi, India
| | - Mitali Kapoor
- Department of Anthropology, University of Delhi, New Delhi, India
| | - Rijith Jayarajan
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Ankit Verma
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | | | - Benrithung Murry
- Department of Anthropology, University of Delhi, New Delhi, India
| | - Sandeep Seth
- Department of Cardiology, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Balram Bhargava
- Department of Cardiology, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Vinod Scaria
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Sridhar Sivasubbu
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Vadlamudi Raghavendra Rao
- Department of Anthropology, University of Delhi, New Delhi, India.
- Department of Genetics, Osmania University, Hyderabad, India.
- Genome Foundation, Hyderabad, 500007, India.
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Wang S, Wang W, Meng Q, Zhou S, Liu H, Ma X, Zhou X, Liu H, Chen X, Jiang W. Inferring Novel Autophagy Regulators Based on Transcription Factors and Non-Coding RNAs Coordinated Regulatory Network. Cells 2018; 7:cells7110194. [PMID: 30400235 PMCID: PMC6262548 DOI: 10.3390/cells7110194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 10/25/2018] [Accepted: 10/30/2018] [Indexed: 01/06/2023] Open
Abstract
Autophagy is a complex cellular digestion process involving multiple regulators. Compared to post-translational autophagy regulators, limited information is now available about transcriptional and post-transcriptional regulators such as transcription factors (TFs) and non-coding RNAs (ncRNAs). In this study, we proposed a computational method to infer novel autophagy-associated TFs, micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs) based on TFs and ncRNAs coordinated regulatory (TNCR) network. First, we constructed a comprehensive TNCR network, including 155 TFs, 681 miRNAs and 1332 lncRNAs. Next, we gathered the known autophagy-associated factors, including TFs, miRNAs and lncRNAs, from public data resources. Then, the random walk with restart (RWR) algorithm was conducted on the TNCR network by using the known autophagy-associated factors as seeds and novel autophagy regulators were finally prioritized. Leave-one-out cross-validation (LOOCV) produced an area under the curve (AUC) of 0.889. In addition, functional analysis of the top 100 ranked regulators, including 55 TFs, 26 miRNAs and 19 lncRNAs, demonstrated that these regulators were significantly enriched in cell death related functions and had significant semantic similarity with autophagy-related Gene Ontology (GO) terms. Finally, extensive literature surveys demonstrated the credibility of the predicted autophagy regulators. In total, we presented a computational method to infer credible autophagy regulators of transcriptional factors and non-coding RNAs, which would improve the understanding of processes of autophagy and cell death and provide potential pharmacological targets to autophagy-related diseases.
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Affiliation(s)
- Shuyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Wencan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Qianqian Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Shunheng Zhou
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
| | - Haizhou Liu
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
| | - Xueyan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Xu Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Hui Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Xiaowen Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Wei Jiang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
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Xi J, Li A, Wang M. A novel unsupervised learning model for detecting driver genes from pan-cancer data through matrix tri-factorization framework with pairwise similarities constraints. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.03.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Liu X, Yang Z, Lin H, Simmons M, Lu Z. DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks. BMC SYSTEMS BIOLOGY 2017; 11:23. [PMID: 28361678 PMCID: PMC5374555 DOI: 10.1186/s12918-017-0402-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND An orphan disease is any disease that affects a small percentage of the population. Orphan diseases are a great burden to patients and society, and most of them are genetic in origin. Unfortunately, our current understanding of the genes responsible for inherited orphan diseases is still quite limited. Developing effective computational algorithms to discover disease-causing genes would help unveil disease mechanisms and may enable better diagnosis and treatment. RESULTS We have developed a novel method, named as DIGNiFI (Disease causIng GeNe FInder), which uses Protein-Protein Interaction (PPI) network-based features to discover and rank candidate disease-causing genes. Specifically, our approach computes topologically similar genes by taking into account both local and global connected paths in PPI networks via Direct Neighbors and Local Random Walks, respectively. Furthermore, since genes with similar phenotypes tend to be functionally related, we have integrated PPI data with gene ontology (GO) annotations and protein complex data to further improve the performance of this approach. Results of 128 orphan diseases with 1184 known disease genes collected from the Orphanet show that our proposed methods outperform existing state-of-the-art methods for discovering candidate disease-causing genes. We also show that further performance improvement can be achieved when enriching the human-curated PPI network data with text-mined interactions from the biomedical literature. Finally, we demonstrate the utility of our approach by applying our method to identifying novel candidate genes for a set of four inherited retinal dystrophies. In this study, we found the top predictions for these retinal dystrophies consistent with literature reports and online databases of other retinal dystrophies. CONCLUSIONS Our method successfully prioritizes orphan-disease-causative genes. This method has great potential to benefit the field of orphan disease research, where resources are scarce and greatly needed.
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Affiliation(s)
- Xiaoxia Liu
- College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, China.,National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health, Bethesda, 20894, MD, USA
| | - Zhihao Yang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, China
| | - Hongfei Lin
- College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, China
| | - Michael Simmons
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health, Bethesda, 20894, MD, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health, Bethesda, 20894, MD, USA.
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Athanasiadis E, Bourdakou M, Spyrou G. D-Map: Random Walking on Gene Network Inference Maps Towards differential Avenue Discovery. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:484-490. [PMID: 26930690 DOI: 10.1109/tcbb.2016.2535267] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Differential rewiring of cellular interaction networks between disease and healthy state is of great importance. Through a systems level approach, malfunctioned mechanisms that are absent in the normal cases, may enlighten the key-players in terms of genes and their interaction chains related to disease. We have developed D-Map, a publicly available user-friendly web application, capable of generating and manipulating advanced differential networks by combining state-of-the-art inference reconstruction methods with random walk simulations. The inputs are expression profiles obtained from the Gene Expression Omnibus and a gene list under investigation. Differential networks may be visualized and interpreted through the use of D-Map interface, where display of the disease, the normal and the common state can be performed, interactively. A case study scenario concerning Alzheimer's disease, as well as breast, lung, and bladder cancer was conducted in order to demonstrate the usefulness of the proposed methodology to different disease types. Findings were consistent with the current bibliography, and the provided interaction lists may be further explored towards novel biological insights of the investigated diseases. The DMap web-application is available at: http://bioserver-3.bioacademy.gr/Bioserver/DMap/index.php.
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Zhang SY, Zhang SW, Liu L, Meng J, Huang Y. m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks. PLoS Comput Biol 2016; 12:e1005287. [PMID: 28027310 PMCID: PMC5226821 DOI: 10.1371/journal.pcbi.1005287] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Revised: 01/11/2017] [Accepted: 12/05/2016] [Indexed: 12/21/2022] Open
Abstract
As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m6A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Specifically, m6A-Driver integrates the PPI network and the predicted differential m6A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m6A-driven network of m6A-driven genes. To evaluate the performance, we applied m6A-Driver to build the context-specific m6A-driven networks for 4 known m6A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation. Powered by methylated RNA immunoprecipitation sequencing (MeRIP-Seq) technology, recent studies have revealed a new mode of post transcriptional regulation mediated by mRNA N6-methyladenosine (m6A). Currently, the analysis of m6A focuses mostly on prediction of m6A sites as well as differential m6A methylation, and systematic approach for predicting m6A functions is yet to emerge. We develop here m6A-Driver, the first network-based approach, to identify m6A-driven genes and their associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Our test results showed that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. m6A-Driver is an effective and reliable approach to identify functionally relevant m6A-driven genes and networks from MeRIP-Seq data.
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Affiliation(s)
- Song-Yao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Shao-Wu Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
- * E-mail: (SWZ); (YH)
| | - Lian Liu
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Jia Meng
- Department of Biological Sciences, HRINU, SUERI, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China
| | - Yufei Huang
- Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America
- * E-mail: (SWZ); (YH)
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Jiang J, Li W, Liang B, Xie R, Chen B, Huang H, Li Y, He Y, Lv J, He W, Chen L. A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism-Related Genes. PLoS One 2016; 11:e0153006. [PMID: 27050193 PMCID: PMC4822849 DOI: 10.1371/journal.pone.0153006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2015] [Accepted: 03/21/2016] [Indexed: 12/13/2022] Open
Abstract
Identifying the genes involved in venous thromboembolism (VTE) recurrence is important not only for understanding the pathogenesis but also for discovering the therapeutic targets. We proposed a novel prioritization method called Function-Interaction-Pearson (FIP) by creating gene-disease similarity scores to prioritize candidate genes underling VTE. The scores were calculated by integrating and optimizing three types of resources including gene expression, gene ontology and protein-protein interaction. As a result, 124 out of top 200 prioritized candidate genes had been confirmed in literature, among which there were 34 antithrombotic drug targets. Compared with two well-known gene prioritization tools Endeavour and ToppNet, FIP was shown to have better performance. The approach provides a valuable alternative for drug targets discovery and disease therapy.
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Affiliation(s)
- Jing Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China, Postal code: 150081
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China, Postal code: 150081
| | - Binhua Liang
- National Microbology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
| | - Ruiqiang Xie
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China, Postal code: 150081
| | - Binbin Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China, Postal code: 150081
| | - Hao Huang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China, Postal code: 150081
| | - Yiran Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China, Postal code: 150081
| | - Yuehan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China, Postal code: 150081
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China, Postal code: 150081
| | - Weiming He
- Institute of Opto-electronics, Harbin Institute of Technology, Harbin, Hei Longjiang Province, China
- * E-mail: (LC); (WH)
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China, Postal code: 150081
- * E-mail: (LC); (WH)
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Du ZP, Wu BL, Xie JJ, Lin XH, Qiu XY, Zhan XF, Wang SH, Shen JH, Li EM, Xu LY. Network Analyses of Gene Expression following Fascin Knockdown in Esophageal Squamous Cell Carcinoma Cells. Asian Pac J Cancer Prev 2015. [DOI: 10.7314/apjcp.2015.16.13.5445] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Muraro D, Lauffenburger DA, Simmons A. Prioritisation and network analysis of Crohn's disease susceptibility genes. PLoS One 2014; 9:e108624. [PMID: 25268122 PMCID: PMC4182533 DOI: 10.1371/journal.pone.0108624] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Accepted: 09/01/2014] [Indexed: 01/23/2023] Open
Abstract
Recent Genome-Wide Association Studies (GWAS) have revealed numerous Crohn's disease susceptibility genes and a key challenge now is in understanding how risk polymorphisms in associated genes might contribute to development of this disease. For a gene to contribute to disease phenotype, its risk variant will likely adversely communicate with a variety of other gene products to result in dysregulation of common signaling pathways. A vital challenge is to elucidate pathways of potentially greatest influence on pathological behaviour, in a manner recognizing how multiple relevant genes may yield integrative effect. In this work we apply mathematical analysis of networks involving the list of recently described Crohn's susceptibility genes, to prioritise pathways in relation to their potential development of this disease. Prioritisation was performed by applying a text mining and a diffusion based method (GRAIL, GPEC). Prospective biological significance of the resulting prioritised list of proteins is highlighted by changes in their gene expression levels in Crohn's patients intestinal tissue in comparison with healthy donors.
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Affiliation(s)
- Daniele Muraro
- Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - Douglas A. Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Alison Simmons
- Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
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Ono T, Kuhara S. A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms. BMC Bioinformatics 2014; 15:179. [PMID: 24917541 PMCID: PMC4068192 DOI: 10.1186/1471-2105-15-179] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Accepted: 06/02/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Understanding the molecular mechanisms involved in disease is critical for the development of more effective and individualized strategies for prevention and treatment. The amount of disease-related literature, including new genetic information on the molecular mechanisms of disease, is rapidly increasing. Extracting beneficial information from literature can be facilitated by computational methods such as the knowledge-discovery approach. Several methods for mining gene-disease relationships using computational methods have been developed, however, there has been a lack of research evaluating specific disease candidate genes. RESULTS We present a novel method for gathering and prioritizing specific disease candidate genes. Our approach involved the construction of a set of Medical Subject Headings (MeSH) terms for the effective retrieval of publications related to a disease candidate gene. Information regarding the relationships between genes and publications was obtained from the gene2pubmed database. The set of genes was prioritized using a "weighted literature score" based on the number of publications and weighted by the number of genes occurring in a publication. Using our method for the disease states of pain and Alzheimer's disease, a total of 1101 pain candidate genes and 2810 Alzheimer's disease candidate genes were gathered and prioritized. The precision was 0.30 and the recall was 0.89 in the case study of pain. The precision was 0.04 and the recall was 0.6 in the case study of Alzheimer's disease. The precision-recall curve indicated that the performance of our method was superior to that of other publicly available tools. CONCLUSIONS Our method, which involved the use of a set of MeSH terms related to disease candidate genes and a novel weighted literature score, improved the accuracy of gathering and prioritizing candidate genes by focusing on a specific disease.
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Affiliation(s)
| | - Satoru Kuhara
- Department of Genetic Resources Technology, Faculty of Agriculture, Kyushu University, 6-10-1 Hakozaki Higashi-ku, Fukuoka 812-8581, Japan.
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