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Yang X, Xu W, Leng D, Wen Y, Wu L, Li R, Huang J, Bo X, He S. Exploring novel disease-disease associations based on multi-view fusion network. Comput Struct Biotechnol J 2023; 21:1807-1819. [PMID: 36923471 PMCID: PMC10009443 DOI: 10.1016/j.csbj.2023.02.038] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 02/02/2023] [Accepted: 02/22/2023] [Indexed: 03/06/2023] Open
Abstract
Established taxonomy system based on disease symptom and tissue characteristics have provided an important basis for physicians to correctly identify diseases and treat them successfully. However, these classifications tend to be based on phenotypic observations, lacking a molecular biological foundation. Therefore, there is an urgent to integrate multi-dimensional molecular biological information or multi-omics data to redefine disease classification in order to provide a powerful perspective for understanding the molecular structure of diseases. Therefore, we offer a flexible disease classification that integrates the biological process, gene expression, and symptom phenotype of diseases, and propose a disease-disease association network based on multi-view fusion. We applied the fusion approach to 223 diseases and divided them into 24 disease clusters. The contribution of internal and external edges of disease clusters were analyzed. The results of the fusion model were compared with Medical Subject Headings, a traditional and commonly used disease taxonomy. Then, experimental results of model performance comparison show that our approach performs better than other integration methods. As it was observed, the obtained clusters provided more interesting and novel disease-disease associations. This multi-view human disease association network describes relationships between diseases based on multiple molecular levels, thus breaking through the limitation of the disease classification system based on tissues and organs. This approach which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies, extends the existing disease taxonomy. Availability of data and materials The preprocessed dataset and source code supporting the conclusions of this article are available at GitHub repository https://github.com/yangxiaoxi89/mvHDN.
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Affiliation(s)
- Xiaoxi Yang
- Clinical Medicine Institute, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.,Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Wenjian Xu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China.,Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.,MOE Key Laboratory of Major Diseases in Children, Beijing 100045, China.,Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing 100045, China
| | - Dongjin Leng
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Lianlian Wu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Ruijiang Li
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jian Huang
- Clinical Medicine Institute, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Xiaochen Bo
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
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Guo A, Khan YM, Langabeer JR, Foraker RE. Discovering disease-disease associations using electronic health records in The Guideline Advantage (TGA) dataset. Sci Rep 2021; 11:20969. [PMID: 34697328 PMCID: PMC8547216 DOI: 10.1038/s41598-021-00345-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 09/22/2021] [Indexed: 11/29/2022] Open
Abstract
Certain diseases have strong comorbidity and co-occurrence with others. Understanding disease-disease associations can potentially increase awareness among healthcare providers of co-occurring conditions and facilitate earlier diagnosis, prevention and treatment of patients. In this study, we utilized the valuable and large The Guideline Advantage (TGA) longitudinal electronic health record dataset from 70 outpatient clinics across the United States to investigate potential disease-disease associations. Specifically, the most prevalent 50 disease diagnoses were manually identified from 165,732 unique patients. To investigate the co-occurrence or dependency associations among the 50 diseases, the categorical disease terms were first mapped into numerical vectors based on disease co-occurrence frequency in individual patients using the Word2Vec approach. Then the novel and interesting disease association clusters were identified using correlation and clustering analyses in the numerical space. Moreover, the distribution of time delay (Δt) between pair-wise strongly associated diseases (correlation coefficients ≥ 0.5) were calculated to show the dependency among the diseases. The results can indicate the risk of disease comorbidity and complications, and facilitate disease prevention and optimal treatment decision-making.
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Affiliation(s)
- Aixia Guo
- Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, USA.
| | - Yosef M Khan
- Health Informatics and Analytics, Centers for Health Metrics and Evaluation, American Heart Association, Dallas, TX, USA
| | - James R Langabeer
- School of Biomedical Informatics, Health Science Center at Houston, The University of Texas, Houston, TX, USA
| | - Randi E Foraker
- Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, USA
- Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, USA
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Perez-Breva L, Shin JH. Artificial Intelligence in Neurosurgery: A Comment on the Possibilities. Neurospine 2019; 16:640-642. [PMID: 31905451 PMCID: PMC6944990 DOI: 10.14245/ns.1938404.202] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Accepted: 11/24/2019] [Indexed: 12/05/2022] Open
Affiliation(s)
- Luis Perez-Breva
- MIT School of Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - John H Shin
- Department of Neurosurgery, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
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Luo L, Zheng C, Wang J, Tan M, Li Y, Xu R. Analysis of disease organ as a novel phenotype towards disease genetics understanding. J Biomed Inform 2019; 95:103235. [PMID: 31207382 PMCID: PMC6644057 DOI: 10.1016/j.jbi.2019.103235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Revised: 06/06/2019] [Accepted: 06/13/2019] [Indexed: 11/24/2022]
Abstract
Discerning the modular nature of human diseases through computational approaches calls for diverse data. The finding sites of diseases, like other disease phenotypes, possess rich information in understanding disease genetics. Yet, analysis of the rich knowledge of disease finding sites has not been comprehensively investigated. In this study, we built a large-scale disease organ network (DON) based on 76,561 disease-organ associations (for 37,615 diseases and 3492 organs) extracted from the United Medical Language System (UMLS) Metathesaurus. We investigated how phenotypic organ similarity among diseases in DON reflects disease gene sharing. We constructed a disease genetic network (DGN) using curated disease-gene associations and demonstrated that disease pairs with higher organ similarities not only are more likely to share genes, but also tend to share more genes. Based on community detection algorithm, we showed that phenotypic disease clusters on DON significantly correlated with genetic disease clusters on DGN. We compared DON with a state-of-art disease phenotype network, disease manifestation network (DMN), that we have recently constructed, and demonstrated that DON contains complementary knowledge for disease genetics understanding.
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Affiliation(s)
- Lingyun Luo
- School of Computer Science, University of South China, Hengyang, Hunan 421001, China; Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio 44106, USA.
| | - Chunlei Zheng
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Jiaolong Wang
- School of Computer Science, University of South China, Hengyang, Hunan 421001, China
| | - Minsheng Tan
- School of Computer Science, University of South China, Hengyang, Hunan 421001, China
| | - Yanshu Li
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Rong Xu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio 44106, USA
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Zheng C, Xu R. Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data. BMC Bioinformatics 2018; 19:500. [PMID: 30591027 PMCID: PMC6309066 DOI: 10.1186/s12859-018-2468-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Background Systems approaches in studying disease relationship have wide applications in biomedical discovery, such as disease mechanism understanding and drug discovery. The FDA Adverse Event Reporting System (FAERS) contains rich information about patient diseases, medications, drug adverse events and demographics of 17 million case reports. Here, we explored this data resource to mine disease comorbidity relationships using association rule mining algorithm and constructed a disease comorbidity network. Results We constructed a disease comorbidity network with 1059 disease nodes and 12,608 edges using association rule mining of FAERS (14,157 rules). We evaluated the performance of comorbidity mining from FAERS using known disease comorbidities of multiple sclerosis (MS), psoriasis and obesity that represent rare, moderate and common disease respectively. Comorbidities of MS, obesity and psoriasis obtained from our network achieved precisions of 58.6%, 73.7%, 56.2% and recalls 87.5%, 69.2% and 72.7% separately. We performed comparative analysis of the disease comorbidity network with disease semantic network, disease genetic network and disease treatment network. We showed that (1) disease comorbidity clusters exhibit significantly higher semantic similarity than random network (0.18 vs 0.10); (2) disease comorbidity clusters share significantly more genes (0.46 vs 0.06); and (3) disease comorbidity clusters share significantly more drugs (0.64 vs 0.17). Finally, we demonstrated that the disease comorbidity network has potential in uncovering novel disease relationships using asthma as a case study. Conclusions Our study presented the first comprehensive attempt to build a disease comorbidity network from FDA Adverse Event Reporting System. This network shows well correlated with disease semantic similarity, disease genetics and disease treatment, which has great potential in disease genetics prediction and drug discovery.
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Affiliation(s)
- Chunlei Zheng
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 2103 Cornell Road, Cleveland, 44106, OH, USA
| | - Rong Xu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 2103 Cornell Road, Cleveland, 44106, OH, USA.
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Brown N, Cambruzzi J, Cox PJ, Davies M, Dunbar J, Plumbley D, Sellwood MA, Sim A, Williams-Jones BI, Zwierzyna M, Sheppard DW. Big Data in Drug Discovery. PROGRESS IN MEDICINAL CHEMISTRY 2018; 57:277-356. [PMID: 29680150 DOI: 10.1016/bs.pmch.2017.12.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.
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Affiliation(s)
| | | | | | | | | | | | | | - Aaron Sim
- BenevolentAI, London, United Kingdom
| | | | - Magdalena Zwierzyna
- BenevolentAI, London, United Kingdom; Institute of Cardiovascular Science, University College London, London, United Kingdom
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Kalgotra P, Sharda R, Croff JM. Examining health disparities by gender: A multimorbidity network analysis of electronic medical record. Int J Med Inform 2017; 108:22-28. [DOI: 10.1016/j.ijmedinf.2017.09.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 09/19/2017] [Accepted: 09/24/2017] [Indexed: 01/22/2023]
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Federer C, Yoo M, Tan AC. Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials. Assay Drug Dev Technol 2016; 14:557-566. [PMID: 27631620 PMCID: PMC5175440 DOI: 10.1089/adt.2016.742] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Drug adverse events (AEs) are a major health threat to patients seeking medical treatment and a significant barrier in drug discovery and development. AEs are now required to be submitted during clinical trials and can be extracted from ClinicalTrials.gov ( https://clinicaltrials.gov/ ), a database of clinical studies around the world. By extracting drug and AE information from ClinicalTrials.gov and structuring it into a database, drug-AEs could be established for future drug development and repositioning. To our knowledge, current AE databases contain mainly U.S. Food and Drug Administration (FDA)-approved drugs. However, our database contains both FDA-approved and experimental compounds extracted from ClinicalTrials.gov . Our database contains 8,161 clinical trials of 3,102,675 patients and 713,103 reported AEs. We extracted the information from ClinicalTrials.gov using a set of python scripts, and then used regular expressions and a drug dictionary to process and structure relevant information into a relational database. We performed data mining and pattern analysis of drug-AEs in our database. Our database can serve as a tool to assist researchers to discover drug-AE relationships for developing, repositioning, and repurposing drugs.
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Affiliation(s)
- Callie Federer
- Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
- Computational Bioscience Graduate Program, Department of Pharmacology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Minjae Yoo
- Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Aik Choon Tan
- Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado
- University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, Colorado
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