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Yu C, Zong H, Chen Y, Zhou Y, Liu X, Lin Y, Li J, Zheng X, Min H, Shen B. PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data. Brief Bioinform 2024; 25:bbae136. [PMID: 38557678 PMCID: PMC10982949 DOI: 10.1093/bib/bbae136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/19/2023] [Accepted: 03/07/2024] [Indexed: 04/04/2024] Open
Abstract
Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.
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Affiliation(s)
- Chunjiang Yu
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
- School of Artificial Intelligence, Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, 215123, China
- Center for Systems Biology, Soochow University, Suzhou, 215006, China
| | - Hui Zong
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yalan Chen
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
- Center for Systems Biology, Soochow University, Suzhou, 215006, China
- Department of Medical Informatics, School of Medicine, Nantong University, Nantong, 226001, China
| | - Yibin Zhou
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, 215011, China
| | - Xingyun Liu
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuxin Lin
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
| | - Jiakun Li
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaonan Zheng
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Hua Min
- Department of Health Administration and Policy, George Mason University, Fairfax, VA, USA
| | - Bairong Shen
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
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Ontology-Driven Knowledge Sharing in Alzheimer’s Disease Research. INFORMATION 2023. [DOI: 10.3390/info14030188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
Abstract
Alzheimer’s disease is a debilitating neurodegenerative condition which is known to be the most common cause of dementia. Despite its rapidly growing prevalence, medicine still lacks a comprehensive definition of the disease. As a result, Alzheimer’s disease remains neither preventable nor curable. In recent years, broad interdisciplinary collaborations in Alzheimer’s disease research are becoming more common. Furthermore, such collaborations have already demonstrated their superiority in addressing the complexity of the disease in innovative ways. However, establishing effective communication and optimal knowledge distribution between researchers and specialists with different expertise and background is not a straightforward task. To address this challenge, we propose the Alzheimer’s disease Ontology for Diagnosis and Preclinical Classification (AD-DPC) as a tool for effective knowledge sharing in interdisciplinary/multidisciplinary teams working on Alzheimer’s disease. It covers six major conceptual groups, namely Alzheimer’s disease pathology, Alzheimer’s disease spectrum, Diagnostic process, Symptoms, Assessments, and Relevant clinical findings. All concepts were annotated with definitions or elucidations and in some cases enriched with synonyms and additional resources. The potential of AD-DPC to support non-medical experts is demonstrated through an evaluation of its usability, applicability and correctness. The results show that the participants in the evaluation process who lack prior medical knowledge can successfully answer Alzheimer’s disease-related questions by interacting with AD-DPC. Furthermore, their perceived level of knowledge in the field increased leading to effective communication with medical experts.
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The Representation of Causality and Causation with Ontologies: A Systematic Literature Review. Online J Public Health Inform 2022; 14:e4. [PMID: 36120162 PMCID: PMC9473331 DOI: 10.5210/ojphi.v14i1.12577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Objective To explore how disease-related causality is formally represented in current ontologies and identify their potential limitations. Methods We conducted a systematic literature search on eight databases (PubMed, Institute of Electrical and Electronic Engendering (IEEE Xplore), Association for Computing Machinery (ACM), Scopus, Web of Science databases, Ontobee, OBO Foundry, and Bioportal. We included studies published between January 1, 1970, and December 9, 2020, that formally represent the notions of causality and causation in the medical domain using ontology as a representational tool. Further inclusion criteria were publication in English and peer-reviewed journals or conference proceedings. Two authors (SS, RM) independently assessed study quality and performed content analysis using a modified validated extraction grid with pre-established categorization. Results The search strategy led to a total of 8,501 potentially relevant papers, of which 50 met the inclusion criteria. Only 14 out of 50 (28%) specified the nature of causation, and only 7 (14%) included clear and non-circular natural language definitions. Although several theories of causality were mentioned, none of the articles offers a widely accepted conceptualization of how causation and causality can be formally represented. Conclusion No current ontology captures the wealth of available concepts of causality. This provides an opportunity for the development of a formal ontology of causation/causality.
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Müller B, Castro LJ, Rebholz-Schuhmann D. Ontology-based identification and prioritization of candidate drugs for epilepsy from literature. J Biomed Semantics 2022; 13:3. [PMID: 35073996 PMCID: PMC8785029 DOI: 10.1186/s13326-021-00258-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 12/14/2021] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Drug repurposing can improve the return of investment as it finds new uses for existing drugs. Literature-based analyses exploit factual knowledge on drugs and diseases, e.g. from databases, and combine it with information from scholarly publications. Here we report the use of the Open Discovery Process on scientific literature to identify non-explicit ties between a disease, namely epilepsy, and known drugs, making full use of available epilepsy-specific ontologies.
Results
We identified characteristics of epilepsy-specific ontologies to create subsets of documents from the literature; from these subsets we generated ranked lists of co-occurring neurological drug names with varying specificity. From these ranked lists, we observed a high intersection regarding reference lists of pharmaceutical compounds recommended for the treatment of epilepsy. Furthermore, we performed a drug set enrichment analysis, i.e. a novel scoring function using an adaptive tuning parameter and comparing top-k ranked lists taking into account the varying length and the current position in the list. We also provide an overview of the pharmaceutical space in the context of epilepsy, including a final combined ranked list of more than 70 drug names.
Conclusions
Biomedical ontologies are a rich resource that can be combined with text mining for the identification of drug names for drug repurposing in the domain of epilepsy. The ranking of the drug names related to epilepsy provides benefits to patients and to researchers as it enables a quick evaluation of statistical evidence hidden in the scientific literature, useful to validate approaches in the drug discovery process.
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McFarthing K, Buff S, Rafaloff G, Dominey T, Wyse RK, Stott SRW. Parkinson's Disease Drug Therapies in the Clinical Trial Pipeline: 2020. JOURNAL OF PARKINSONS DISEASE 2021; 10:757-774. [PMID: 32741777 PMCID: PMC7458531 DOI: 10.3233/jpd-202128] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background: The majority of current pharmacological treatments for Parkinson’s disease (PD) were approved for clinical use in the second half of the last century and they only provide symptomatic relief. Derivatives of these therapies continue to be explored in clinical trials, together with potentially disease modifying therapies that can slow, stop or reverse the condition. Objective: To provide an overview of the pharmacological therapies— both symptomatic and disease modifying— currently being clinically evaluated for PD, with the goal of creating greater awareness and opportunities for collaboration amongst commercial and academic researchers as well as between the research and patient communities. Methods: We conducted a review of clinical trials of drug therapies for PD using trial data obtained from the ClinicalTrials.gov database and performed a breakdown analysis of studies that were active as of January 21, 2020. Results: We identified 145 registered and ongoing clinical trials for therapeutics targeting PD, of which 51 were Phase 1 (35% of the total number of trials), 66 were Phase 2 (46% ), and 28 were Phase 3 (19% ). There were 57 trials (39% ) focused on long-term disease modifying therapies, with the remaining 88 trials (61% ) focused on therapies for symptomatic relief. A total of 50 (34% ) trials were testing repurposed therapies. Conclusion: There is a broad pipeline of both symptomatic and disease modifying therapies currently being tested in clinical trials for PD.
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Affiliation(s)
| | - Sue Buff
- Parkinson's Research Advocate, Sunnyvale, CA, USA
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Abstract
Introduction Ontology-based annotation of evidence, using disease-specific ontologies, can accelerate analysis and interpretation of the knowledge domain of diseases. Although many domain-specific disease ontologies have been developed so far, in the area of cardiovascular diseases, there is a lack of ontological representation of the disease knowledge domain of stroke. Methods The stroke ontology (STO) was created on the basis of the ontology development life cycle and was built using Protégé ontology editor in the ontology web language format. The ontology was evaluated in terms of structural and functional features, expert evaluation, and competency questions. Results The stroke ontology covers a broad range of major biomedical and risk factor concepts. The majority of concepts are enriched by synonyms, definitions, and references. The ontology attempts to incorporate different users’ views on the stroke domain such as neuroscientists, molecular biologists, and clinicians. Evaluation of the ontology based on natural language processing showed a high precision (0.94), recall (0.80), and F-score (0.78) values, indicating that STO has an acceptable coverage of the stroke knowledge domain. Performance evaluation using competency questions designed by a clinician showed that the ontology can be used to answer expert questions in light of published evidence. Conclusions The stroke ontology is the first, multiple-view ontology in the domain of brain stroke that can be used as a tool for representation, formalization, and standardization of the heterogeneous data related to the stroke domain. Since this is a draft version of the ontology, the contribution of the stroke scientific community can help to improve the usability of the current version.
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Use of a modular ontology and a semantic annotation tool to describe the care pathway of patients with amyotrophic lateral sclerosis in a coordination network. PLoS One 2021; 16:e0244604. [PMID: 33406098 PMCID: PMC7787442 DOI: 10.1371/journal.pone.0244604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 12/11/2020] [Indexed: 11/19/2022] Open
Abstract
The objective of this study was to describe the care pathway of patients with amyotrophic lateral sclerosis (ALS) based on real-life textual data from a regional coordination network, the Ile-de-France ALS network. This coordination network provides care for 92% of patients diagnosed with ALS living in Ile-de-France. We developed a modular ontology (OntoPaRON) for the automatic processing of these unstructured textual data. OntoPaRON has different modules: the core, medical, socio-environmental, coordination, and consolidation modules. Our approach was unique in its creation of fully defined concepts at different levels of the modular ontology to address specific topics relating to healthcare trajectories. We also created a semantic annotation tool specific to the French language and the specificities of our corpus, the Ontology-Based Semantic Annotation Module (OnBaSAM), using the OntoPaRON ontology as a reference. We used these tools to annotate the records of 928 patients automatically. The semantic (qualitative) annotations of the concepts were transformed into quantitative data. By using these pipelines we were able to transform unstructured textual data into structured quantitative data. Based on data processing, semantic annotations, sociodemographic data for the patient and clinical variables, we found that the need and demand for human and technical assistance depend on the initial form of the disease, the motor state, and the patient age. The presence of exhaustion in care management, is related to the patient’s motor and cognitive state.
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PCLiON: An Ontology for Data Standardization and Sharing of Prostate Cancer Associated Lifestyles. Int J Med Inform 2020; 145:104332. [PMID: 33186790 DOI: 10.1016/j.ijmedinf.2020.104332] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 10/28/2020] [Accepted: 11/03/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND Researches on Lifestyle medicine (LM) have emerged in recent years to garner wide attention. Prostate cancer (PCa) could be prevented and treated by positive lifestyles, but the association between lifestyles and PCa is always personalized. OBJECTIVES In order to solve the heterogeneity and diversity of different data types related to PCa, establish a standardized lifestyle ontology, promote the exchange and sharing of disease lifestyle knowledge, and support text mining and knowledge discovery. METHODS The overall construction of PCLiON was created in accordance with the principles and methodology of ontology construction. Following the principles of evidence-based medicine, we screened and integrated the lifestyles and their related attributes. Protégé was used to construct and validate the semantic framework. All annotations in PCLiON were based on SNOMED CT, NCI Thesaurus, the Cochrane Library and FooDB, etc. HTML5 and ASP.NET was used to develop the independent Web page platform and corresponding intelligent terminal application. The PCLiON also uploaded to the National Center for Biomedical Ontology BioPortal. RESULTS PCLiON integrates 397 lifestyles and lifestyle-related factors associated with PCa, and is the first of its kind for a specific disease. It contains 320 attribute annotations and 11 object attributes. The logical relationship and completeness meet the ontology requirements. Qualitative analysis was carried out for 329 terms in PCLiON, including factors which are protective, risk or associated but functional unclear, etc. PCLiON is publicly available both at http://pcaontology.net/PCaLifeStyleDefault.aspx and https://bioportal.bioontology.org/ontologies/PCALION. CONCLUSIONS Through the bilingual online platforms, complex lifestyle research data can be transformed into standardized, reliable and responsive knowledge, which can promote the shared-decision making (SDM) on lifestyle intervention and assist patients in lifestyle self-management toward the goal of PCa targeted prevention.
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Nan LP, Wang F, Ran D, Zhou SF, Liu Y, Zhang Z, Huang ZN, Wang ZY, Wang JC, Feng XM, Zhang L. Naringin alleviates H 2O 2-induced apoptosis via the PI3K/Akt pathway in rat nucleus pulposus-derived mesenchymal stem cells. Connect Tissue Res 2020; 61:554-567. [PMID: 31294637 DOI: 10.1080/03008207.2019.1631299] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Purpose: To investigate the protective effect of naringin (Nar) on H2O2-induced apoptosis of nucleus pulposus-derived mesenchymal stem cells (NPMSC) and the potential mechanism in this process. Methods: Rat NPMSC were cultured in MSC culture medium or culture medium with different concentrations of H2O2. Nar or the combination of Nar and LY294002 was added into the culture medium to investigate the effects of Nar. Cell viability was evaluated by cell counting kit-8 (CCK-8) assay. The apoptosis rate was determined using Annexin V/PI dual staining and terminal deoxynucleotide transferase-mediated dUTP nick end labeling (TUNEL) assays. Additionally, the levels of reactive oxygen species (ROS) and mitochondrial membrane potential (MMP) were analyzed by flow cytometry. ATP level in NPMSC was analyzed via ATP detection kit. Mitochondrial ultrastructure change was observed through transmission electron microscope (TEM). Levels of apoptosis-associated molecules (cleaved caspase-3, Bax and Bcl-2) were evaluated via RT-PCR and western blot, respectively. Results: The cells isolated from NP met the criteria for MSC. H2O2 significantly promoted NPMSC apoptosis in a dose and time-dependent manner. Nar showed no cytotoxicity effect on NPMSC up to a concentration of 100 μM for 24 h. Nar exhibited protective effects against H2O2-induced NPMSC apoptosis including apoptosis rate, expressions of proapoptosis and antiapoptosis related genes and protein. Nar could also alleviate H2O2-induced mitochondrial dysfunction of increased mitochondrial ROS production, reduced MMP, decreased intracellular ATP and mitochondrial ultrastructure change. However, these protected effects were inhibited after LY294002 treatment. Conclusions: Our results demonstrated that Nar efficiently attenuated H2O2-induced NPMSC apoptosis and mitochondrial dysfunction. The activation of ROS-mediated PI3K/Akt pathway may be the potential mechanism in this process.
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Affiliation(s)
- Li-Ping Nan
- Department of Orthopedics, Dalian Medical University , Dalian, Liaoning, China.,Department of Orthopedics, Clinical Medical College of Yangzhou University , Yangzhou, Jiangsu, China
| | - Feng Wang
- Department of Orthopedics, Dalian Medical University , Dalian, Liaoning, China.,Department of Orthopedics, Clinical Medical College of Yangzhou University , Yangzhou, Jiangsu, China
| | - Di Ran
- College of Veterinary Medicine, Yangzhou University , Yangzhou, China
| | - Shi-Feng Zhou
- Department of Orthopedics, Clinical Medical College of Yangzhou University , Yangzhou, Jiangsu, China
| | - Yang Liu
- Department of Orthopedics, Dalian Medical University , Dalian, Liaoning, China.,Department of Orthopedics, Clinical Medical College of Yangzhou University , Yangzhou, Jiangsu, China
| | - Zhen Zhang
- Department of Orthopedics, Dalian Medical University , Dalian, Liaoning, China.,Department of Orthopedics, Clinical Medical College of Yangzhou University , Yangzhou, Jiangsu, China
| | - Ze-Nan Huang
- Department of Orthopedics, Clinical Medical College of Yangzhou University , Yangzhou, Jiangsu, China
| | - Ze-Yu Wang
- Department of Orthopedics, Clinical Medical College of Yangzhou University , Yangzhou, Jiangsu, China
| | - Jing-Cheng Wang
- Department of Orthopedics, Clinical Medical College of Yangzhou University , Yangzhou, Jiangsu, China
| | - Xin-Min Feng
- Department of Orthopedics, Clinical Medical College of Yangzhou University , Yangzhou, Jiangsu, China
| | - Liang Zhang
- Department of Orthopedics, Clinical Medical College of Yangzhou University , Yangzhou, Jiangsu, China
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Subirats L, Conesa J, Armayones M. Biomedical Holistic Ontology for People with Rare Diseases. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E6038. [PMID: 32825147 PMCID: PMC7503469 DOI: 10.3390/ijerph17176038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 07/24/2020] [Accepted: 07/29/2020] [Indexed: 11/16/2022]
Abstract
This research provides a biomedical ontology to adequately represent the information necessary to manage a person with a disease in the context of a specific patient. A bottom-up approach was used to build the ontology, best ontology practices described in the literature were followed and the minimum information to reference an external ontology term (MIREOT) methodology was used to add external terms of other ontologies when possible. Public data of rare diseases from rare associations were used to build the ontology. In addition, sentiment analysis was performed in the standardized data using the Python library Textblob. A new holistic ontology was built, which models 25 real scenarios of people with rare diseases. We conclude that a comprehensive profile of patients is needed in biomedical ontologies. The generated code is openly available, so this research is partially reproducible. Depending on the knowledge needed, several views of the ontology should be generated. Links to other ontologies should be used more often to model the knowledge more precisely and improve flexibility. The proposed holistic ontology has many benefits, such as a more standardized computation of sentiment analysis between attributes.
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Affiliation(s)
- Laia Subirats
- Eurecat, Centre Tecnològic de Catalunya, C/Bilbao, 72, 08005 Barcelona, Spain
- eHealth Center, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018 Barcelona, Spain; (J.C.); (M.A.)
| | - Jordi Conesa
- eHealth Center, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018 Barcelona, Spain; (J.C.); (M.A.)
| | - Manuel Armayones
- eHealth Center, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018 Barcelona, Spain; (J.C.); (M.A.)
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Liu Y, Li Y, Huang ZN, Wang ZY, Nan LP, Wang F, Zhou SF, Wang JC, Feng XM, Zhang L. The effect of intervertebral disc degenerative change on biological characteristics of nucleus pulposus mesenchymal stem cell: an in vitro study in rats. Connect Tissue Res 2019; 60:376-388. [PMID: 31119993 DOI: 10.1080/03008207.2019.1570168] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Purpose: To evaluate the change on biological characteristics of mesenchymal stem cell (MSC) derived from normal and degenerative intervertebral disc (IVD). Methods: MSC was isolated from normal and degenerative IVD rat model. Immunophenotype detected by flow cytometric analysis, expression of stemness genes determined by reverse-transcription polymerase chain reaction (RT-PCR) and osteogenic, adipogenic and chondrogenic differentiation were compared between MSC derived from normal IVD (N-NPMSC) and degenerative IVD (D-NPMSC). The biological characteristics including cell proliferation, colony formation, apoptosis, caspase-3 activity and mRNA and protein expressions of hypoxia inducible factor-1α (HIF-1α), glucose transporter 1 (GLUT-1), vascular endothelial growth factor (VEGF), silent information regulator protein 1 (SIRT1) and silent information regulator protein 6 (SIRT6) were compared between N-NPMSC and D-NPMSC. Results: Both of N-NPMSC and D-NPMSC highly expressed CD105, CD90 and CD73, and lower expressed CD34 and CD45. There was no significant difference in cell morphology and multipotent differentiation ability between N-NPMSC and D-NPMSC. D-NPMSC showed significantly lower expressions of stemness genes, cell proliferation and colony formation ability. D-NPMSC also exhibited increased cell apoptosis rate and caspase-3 expression, and significantly lower expressions of HIF-1α, GLUT-1, VEGF, SIRT1 and SIRT6 in mRNA and protein levels compared with N-NPMSC. Conclusions: N-NPMSC showed significantly higher proliferation rate, better colony forming and stemness maintenance ability, whereas reduced cell apoptosis rate compared with D-NPMSC. HIF-1α-mediated signal pathway may be involved in the regulation of NPMSC proliferation. These findings indicated that degenerative change of IVD should be taken into account when selecting a source of NPMSC for clinical application.
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Affiliation(s)
- Yang Liu
- a Department of Orthopedics , Dalian Medical University , Dalian , Liaoning , China
| | - Yan Li
- b Department of Internal Medicine , Dalian Medical University , Dalian , Liaoning , China
| | - Ze-Nan Huang
- c Department of Orthopedics , Clinical Medical College of Yangzhou University , Yangzhou , Jiangsu , People's Republic of China
| | - Ze-Yu Wang
- c Department of Orthopedics , Clinical Medical College of Yangzhou University , Yangzhou , Jiangsu , People's Republic of China
| | - Li-Ping Nan
- a Department of Orthopedics , Dalian Medical University , Dalian , Liaoning , China
| | - Feng Wang
- a Department of Orthopedics , Dalian Medical University , Dalian , Liaoning , China
| | - Shi-Feng Zhou
- c Department of Orthopedics , Clinical Medical College of Yangzhou University , Yangzhou , Jiangsu , People's Republic of China
| | - Jing-Cheng Wang
- c Department of Orthopedics , Clinical Medical College of Yangzhou University , Yangzhou , Jiangsu , People's Republic of China
| | - Xin-Min Feng
- c Department of Orthopedics , Clinical Medical College of Yangzhou University , Yangzhou , Jiangsu , People's Republic of China
| | - Liang Zhang
- c Department of Orthopedics , Clinical Medical College of Yangzhou University , Yangzhou , Jiangsu , People's Republic of China
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Haendel MA, McMurry JA, Relevo R, Mungall CJ, Robinson PN, Chute CG. A Census of Disease Ontologies. Annu Rev Biomed Data Sci 2018. [DOI: 10.1146/annurev-biodatasci-080917-013459] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
For centuries, humans have sought to classify diseases based on phenotypic presentation and available treatments. Today, a wide landscape of strategies, resources, and tools exist to classify patients and diseases. Ontologies can provide a robust foundation of logic for precise stratification and classification along diverse axes such as etiology, development, treatment, and genetics. Disease and phenotype ontologies are used in four primary ways: ( a) search, retrieval, and annotation of knowledge; ( b) data integration and analysis; ( c) clinical decision support; and ( d) knowledge discovery. Computational inference can connect existing knowledge and generate new insights and hypotheses about drug targets, prognosis prediction, or diagnosis. In this review, we examine the rise of disease and phenotype ontologies and the diverse ways they are represented and applied in biomedicine.
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Affiliation(s)
- Melissa A. Haendel
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon 97239, USA
- Linus Pauling Institute, Oregon State University, Corvallis, Oregon 97331, USA
| | - Julie A. McMurry
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon 97239, USA
| | - Rose Relevo
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon 97239, USA
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | | | - Christopher G. Chute
- School of Medicine, School of Public Health, and School of Nursing, Johns Hopkins University, Baltimore, Maryland 21205, USA
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Geerts H, Hofmann-Apitius M, Anastasio TJ. Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends. Alzheimers Dement 2017; 13:1292-1302. [PMID: 28917669 DOI: 10.1016/j.jalz.2017.08.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 07/05/2017] [Accepted: 08/01/2017] [Indexed: 11/24/2022]
Abstract
Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD.
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Affiliation(s)
- Hugo Geerts
- In Silico Biosciences, Berwyn, PA, USA; Perelman School of Medicine, Univ. of Pennsylvania.
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Thomas J Anastasio
- Department of Molecular and Integrative Physiology, and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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Iyappan A, Younesi E, Redolfi A, Vrooman H, Khanna S, Frisoni GB, Hofmann-Apitius M. Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features. J Alzheimers Dis 2017; 59:1153-1169. [PMID: 28731430 PMCID: PMC5611802 DOI: 10.3233/jad-161148] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.
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Affiliation(s)
- Anandhi Iyappan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
| | - Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany
| | - Alberto Redolfi
- Laboratory of Epidemiology and Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Henri Vrooman
- Departments of Radiology and Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC University Medical Center, The Netherlands
| | - Shashank Khanna
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
| | - Giovanni B Frisoni
- Laboratory of Epidemiology and Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and Laboratoire de Neuroimagerie du Vieillissement (LANVIE), University Hospitals and University of Geneva, Geneva, Switzerland
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
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Talikka M, Bukharov N, Hayes WS, Hofmann-Apitius M, Alexopoulos L, Peitsch MC, Hoeng J. Novel approaches to develop community-built biological network models for potential drug discovery. Expert Opin Drug Discov 2017; 12:849-857. [PMID: 28585481 DOI: 10.1080/17460441.2017.1335302] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
INTRODUCTION Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.
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Affiliation(s)
- Marja Talikka
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
| | - Natalia Bukharov
- b Translational Data Management Services, Clarivate Analytics (Formerly the IP & Science Business of Thomson Reuters) , Boston , MA , USA
| | - William S Hayes
- c Data Sciences , Applied Dynamic Solutions, LLC , Rahway , NJ , USA
| | - Martin Hofmann-Apitius
- d Department of Bioinformatics , Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven , Sankt Augustin , Germany
| | - Leonidas Alexopoulos
- e Systems Bioengineering Lab , National Technical University of Athens , Zografou , Greece.,f Protavio Ltd , Stevenage , UK
| | - Manuel C Peitsch
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
| | - Julia Hoeng
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
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Wang L, Haug PJ, Del Fiol G. Using classification models for the generation of disease-specific medications from biomedical literature and clinical data repository. J Biomed Inform 2017; 69:259-266. [PMID: 28435015 PMCID: PMC5509335 DOI: 10.1016/j.jbi.2017.04.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Revised: 04/03/2017] [Accepted: 04/19/2017] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise. It is unreliable to determine the relevance of the association by simply setting up arbitrary cut-off points on multiple scores of relevance; and it would be expensive to ask human experts to manually review a large number of associations. We propose that machine-learning-based classification can be used to separate the signal from the noise, and to provide a feasible approach to create and maintain disease-specific vocabularies. METHOD We initially focused on disease-medication associations for the purpose of simplicity. For a disease of interest, we extracted potentially treatment-related drug concepts from biomedical literature citations and from a local clinical data repository. Each concept was associated with multiple measures of relevance (i.e., features) such as frequency of occurrence. For the machine purpose of learning, we formed nine datasets for three diseases with each disease having two single-source datasets and one from the combination of previous two datasets. All the datasets were labeled using existing reference standards. Thereafter, we conducted two experiments: (1) to test if adding features from the clinical data repository would improve the performance of classification achieved using features from the biomedical literature only, and (2) to determine if classifier(s) trained with known medication-disease data sets would be generalizable to new disease(s). RESULTS Simple logistic regression and LogitBoost were two classifiers identified as the preferred models separately for the biomedical-literature datasets and combined datasets. The performance of the classification using combined features provided significant improvement beyond that using biomedical-literature features alone (p-value<0.001). The performance of the classifier built from known diseases to predict associated concepts for new diseases showed no significant difference from the performance of the classifier built and tested using the new disease's dataset. CONCLUSION It is feasible to use classification approaches to automatically predict the relevance of a concept to a disease of interest. It is useful to combine features from disparate sources for the task of classification. Classifiers built from known diseases were generalizable to new diseases.
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Affiliation(s)
- Liqin Wang
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA; Homer Warner Research Center, Intermountain Healthcare, 5121 South Cottonwood Street, Murray, UT 84107, USA.
| | - Peter J Haug
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA; Homer Warner Research Center, Intermountain Healthcare, 5121 South Cottonwood Street, Murray, UT 84107, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA
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Hampel H, O’Bryant SE, Durrleman S, Younesi E, Rojkova K, Escott-Price V, Corvol JC, Broich K, Dubois B, Lista S. A Precision Medicine Initiative for Alzheimer’s disease: the road ahead to biomarker-guided integrative disease modeling. Climacteric 2017; 20:107-118. [DOI: 10.1080/13697137.2017.1287866] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- H. Hampel
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - S. E. O’Bryant
- Institute for Healthy Aging, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - S. Durrleman
- ARAMIS Lab, Inria Paris, Paris, France
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du cerveau et la moelle (ICM), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, Paris, France
| | - E. Younesi
- European Society for Translational Medicine, Vienna, Austria
| | - K. Rojkova
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - V. Escott-Price
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - J-C. Corvol
- Département de Neurologie, Sorbonne Université, Université Pierre et Marie Curie, Paris 06 UMR S 1127, Institut National de Santé et en Recherche Médicale (INSERM) U 1127 and CIC-1422, Centre National de Recherche Scientifique U 7225, Institut du Cerveau et de la Moelle Epinière, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France
| | - K. Broich
- President, Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
| | - B. Dubois
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - S. Lista
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
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Ruiz-Cerdá ML, Irurzun-Arana I, González-Garcia I, Hu C, Zhou H, Vermeulen A, Trocóniz IF, Gómez-Mantilla JD. Towards patient stratification and treatment in the autoimmune disease lupus erythematosus using a systems pharmacology approach. Eur J Pharm Sci 2016; 94:46-58. [DOI: 10.1016/j.ejps.2016.04.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 04/07/2016] [Accepted: 04/07/2016] [Indexed: 01/28/2023]
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Hofmann-Apitius M, Ball G, Gebel S, Bagewadi S, de Bono B, Schneider R, Page M, Kodamullil AT, Younesi E, Ebeling C, Tegnér J, Canard L. Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders. Int J Mol Sci 2015; 16:29179-206. [PMID: 26690135 PMCID: PMC4691095 DOI: 10.3390/ijms161226148] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Revised: 11/10/2015] [Accepted: 11/12/2015] [Indexed: 12/22/2022] Open
Abstract
Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).
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Affiliation(s)
- Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
- Rheinische Friedrich-Wilhelms-Universitaet Bonn, University of Bonn, Bonn 53113, Germany.
| | - Gordon Ball
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, and Unit of Clinical Epidemiology, Karolinska University Hospital, Stockholm SE-171 77, Sweden.
- Science for Life Laboratories, Karolinska Institutet, Stockholm SE-171 77, Sweden.
| | - Stephan Gebel
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, Esch-sur-Alzette L-4362, Luxembourg.
| | - Shweta Bagewadi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
| | - Bernard de Bono
- Institute of Health Informatics, University College London, London NW1 2DA, UK.
- Auckland Bioengineering Institute, University of Auckland, Symmonds Street, Auckland 1142, New Zealand.
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, Esch-sur-Alzette L-4362, Luxembourg.
| | - Matt Page
- Translational Bioinformatics, UCB Pharma, 216 Bath Rd, Slough SL1 3WE, UK.
| | - Alpha Tom Kodamullil
- Rheinische Friedrich-Wilhelms-Universitaet Bonn, University of Bonn, Bonn 53113, Germany.
| | - Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
| | - Christian Ebeling
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
| | - Jesper Tegnér
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, and Unit of Clinical Epidemiology, Karolinska University Hospital, Stockholm SE-171 77, Sweden.
- Science for Life Laboratories, Karolinska Institutet, Stockholm SE-171 77, Sweden.
| | - Luc Canard
- Translational Science Unit, SANOFI Recherche & Développement, 1 Avenue Pierre Brossolette, Chilly-Mazarin Cedex 91385, France.
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