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Bartnik A, Serra LM, Smith M, Duncan WD, Wishnie L, Ruttenberg A, Dwyer MG, Diehl AD. MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology. Neuroinformatics 2024:10.1007/s12021-024-09664-8. [PMID: 38763990 DOI: 10.1007/s12021-024-09664-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/22/2024] [Indexed: 05/21/2024]
Abstract
Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.
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Affiliation(s)
- Alexander Bartnik
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Lucas M Serra
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Mackenzie Smith
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - William D Duncan
- College of Dentistry, University of Florida, Gainesville, FL, USA
| | - Lauren Wishnie
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Alan Ruttenberg
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Alexander D Diehl
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
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Bernabé CH, Queralt-Rosinach N, Silva Souza VE, Bonino da Silva Santos LO, Mons B, Jacobsen A, Roos M. The use of foundational ontologies in biomedical research. J Biomed Semantics 2023; 14:21. [PMID: 38082345 PMCID: PMC10712036 DOI: 10.1186/s13326-023-00300-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests organising ontologies in levels, where domain specific (low-level) ontologies are grounded in domain independent high-level ontologies (i.e., foundational ontologies). In this level-based organisation, foundational ontologies work as translators of intended meaning, thus improving interoperability. Despite their considerable acceptance in biomedical research, there are very few studies testing foundational ontologies. This paper describes a systematic literature mapping that was conducted to understand how foundational ontologies are used in biomedical research and to find empirical evidence supporting their claimed (dis)advantages. RESULTS From a set of 79 selected papers, we identified that foundational ontologies are used for several purposes: ontology construction, repair, mapping, and ontology-based data analysis. Foundational ontologies are claimed to improve interoperability, enhance reasoning, speed up ontology development and facilitate maintainability. The complexity of using foundational ontologies is the most commonly cited downside. Despite being used for several purposes, there were hardly any experiments (1 paper) testing the claims for or against the use of foundational ontologies. In the subset of 49 papers that describe the development of an ontology, it was observed a low adherence to ontology construction (16 papers) and ontology evaluation formal methods (4 papers). CONCLUSION Our findings have two main implications. First, the lack of empirical evidence about the use of foundational ontologies indicates a need for evaluating the use of such artefacts in biomedical research. Second, the low adherence to formal methods illustrates how the field could benefit from a more systematic approach when dealing with the development and evaluation of ontologies. The understanding of how foundational ontologies are used in the biomedical field can drive future research towards the improvement of ontologies and, consequently, data FAIRness. The adoption of formal methods can impact the quality and sustainability of ontologies, and reusing these methods from other fields is encouraged.
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Affiliation(s)
- César H Bernabé
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
| | | | | | - Luiz Olavo Bonino da Silva Santos
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- University of Twente, Enschede, The Netherlands
| | - Barend Mons
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Annika Jacobsen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Marco Roos
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
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3
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Bartnik A, Serra LM, Smith M, Duncan WD, Wishnie L, Ruttenberg A, Dwyer MG, Diehl AD. MRIO: The Magnetic Resonance Imaging Acquisition and Analysis Ontology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.04.552020. [PMID: 37609265 PMCID: PMC10441376 DOI: 10.1101/2023.08.04.552020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Objective Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. Materials and Methods To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. Results MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. Discussion MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principals and has contributed several terms to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. Conclusion MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.
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Affiliation(s)
- Alexander Bartnik
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Lucas M. Serra
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Mackenzie Smith
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | | | - Lauren Wishnie
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Alan Ruttenberg
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Michael G. Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Alexander D. Diehl
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
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Hung YP, Chen PL, Ho CY, Hsieh CC, Lee CH, Lee CC, Ko WC. Prognostic Effects of Inappropriate Empirical Antimicrobial Therapy in Adults With Community-Onset Bacteremia: Age Matters. Front Med (Lausanne) 2022; 9:861032. [PMID: 35479958 PMCID: PMC9037591 DOI: 10.3389/fmed.2022.861032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background Studies have reported the effects of delayed administration of appropriate antimicrobial therapy (AAT) on the short-term prognosis of patients with bloodstream infections; however, whether there is an age-related difference in these effects remains debated. Methods In this 4-year multicenter case-control study, patients with community-onset bacteremia were retrospectively categorized into the "middle-aged" (45-64 years), "old" (65-74 years), and "very old" (≥75 years) groups. Two methods were adopted to investigate the prognostic effects of delayed AAT in each age group. First, its effects were, respectively, investigated, after adjustment for the independent predictors of 30-day mortality. Second, patients in each age group were matched by the closest propensity-score (PS), which was calculated by independent predictors of mortality; the survival curves and Pearson chi-square tests were adopted to disclose its effects in each PS-matching group. Results Each hour of delayed AAT resulted in an average increase in the 30-day crude mortality rate of 0.2% (P = 0.03), 0.4% (P < 0.001), and 0.7% (P < 0.001) in middle-aged (968 patients), old (683), and very old (1,265) patients, after, respectively, adjusting the independent predictors of mortality in each group. After appropriate PS-matching, no significant proportion differences in patient demographics, bacteremia characteristics, severity of bacteremia and comorbidities, and 15-day or 30-day crude mortality rates were observed between three matched groups (582 patients in each group). However, significant differences in survival curves between patients with delayed AAT > 24 or >48 h and those without delayed administration were demonstrated in each age group. Furthermore, the odds ratios of 30-day mortality for delayed AAT > 24 or >48 h were 1.73 (P = 0.04) or 1.82 (P = 0.04), 1.84 (P = 0.03) or 1.95 (P = 0.02), and 1.87 (P = 0.02) or 2.34 (P = 0.003) in the middle-aged, old, and very old groups, respectively. Notably, the greatest prognostic impact of delayed AAT > 24 or >48 h in the very old group and the smallest impact in the middle-aged group were exhibited. Conclusion For adults (aged ≥45 years) with community-onset bacteremia, the delayed AAT significantly impacts their short-term survival in varied age groups and the age-related differences in its prognostic impact might be evident.
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Affiliation(s)
- Yuan-Pin Hung
- Department of Internal Medicine, Tainan Hospital, Ministry of Health and Welfare, Tainan, Taiwan.,Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Po-Lin Chen
- Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ching-Yu Ho
- Department of Adult Critical Care Medicine, Tainan Sin-Lau Hospital, Tainan, Taiwan.,Department of Nursing, National Tainan Junior College of Nursing, Tainan, Taiwan
| | - Chih-Chia Hsieh
- Department of Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chung-Hsun Lee
- Department of Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ching-Chi Lee
- Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Clinical Medicine Research Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Wen-Chien Ko
- Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
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5
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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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Gomez-Valades A, Martinez-Tomas R, Rincon M. Integrative Base Ontology for the Research Analysis of Alzheimer's Disease-Related Mild Cognitive Impairment. Front Neuroinform 2021; 15:561691. [PMID: 33613222 PMCID: PMC7889797 DOI: 10.3389/fninf.2021.561691] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 01/12/2021] [Indexed: 11/13/2022] Open
Abstract
Early detection of mild cognitive impairment (MCI) has become a priority in Alzheimer's disease (AD) research, as it is a transitional phase between normal aging and dementia. However, information on MCI and AD is scattered across different formats and standards generated by different technologies, making it difficult to work with them manually. Ontologies have emerged as a solution to this problem due to their capacity for homogenization and consensus in the representation and reuse of data. In this context, an ontology that integrates the four main domains of neurodegenerative diseases, diagnostic tests, cognitive functions, and brain areas will be of great use in research. Here, we introduce the first approach to this ontology, the Neurocognitive Integrated Ontology (NIO), which integrates the knowledge regarding neuropsychological tests (NT), AD, cognitive functions, and brain areas. This ontology enables interoperability and facilitates access to data by integrating dispersed knowledge across different disciplines, rendering it useful for other research groups. To ensure the stability and reusability of NIO, the ontology was developed following the ontology-building life cycle, integrating and expanding terms from four different reference ontologies. The usefulness of this ontology was validated through use-case scenarios.
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Affiliation(s)
- Alba Gomez-Valades
- Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED) Madrid, Spain
| | - Rafael Martinez-Tomas
- Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED) Madrid, Spain
| | - Mariano Rincon
- Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED) Madrid, Spain
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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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8
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Alshamrani R, Althbiti A, Alshamrani Y, Alkomah F, Ma X. Model-Driven Decision Making in Multiple Sclerosis Research: Existing Works and Latest Trends. PATTERNS 2020; 1:100121. [PMID: 33294867 PMCID: PMC7691382 DOI: 10.1016/j.patter.2020.100121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Multiple sclerosis (MS) is a neurological disorder that strikes the central nervous system. Due to the complexity of this disease, healthcare sectors are increasingly in need of shared clinical decision-making tools to provide practitioners with insightful knowledge and information about MS. These tools ought to be comprehensible by both technical and non-technical healthcare audiences. To aid this cause, this literature review analyzes the state-of-the-art decision support systems (DSSs) in MS research with a special focus on model-driven decision-making processes. The review clusters common methodologies used to support the decision-making process in classifying, diagnosing, predicting, and treating MS. This work observes that the majority of the investigated DSSs rely on knowledge-based and machine learning (ML) approaches, so the utilization of ontology and ML in the MS domain is observed to extend the scope of this review. Finally, this review summarizes the state-of-the-art DSSs, discusses the methods that have commonalities, and addresses the future work of applying DSS technologies in the MS field. Multiple sclerosis (MS) is a disorder that strikes the central nervous system of the human body. This article reviews state-of-the-art decision support systems (DSSs) in MS research, as recent studies have highlighted the importance of DSSs in the medical realm. However, the utilization of decision support systems for MS remains an open challenge. A special focus in this article is given to model-driven DSSs, which uses knowledge representation to simplify the complex process for decision makers. We find that most investigated studies use knowledge-based and machine learning approaches. Based on the literature review, we suggest some future work of applying DSSs in the MS domain. Potential future directions should focus on applying DSS technologies to understand the MS patterns, etiology, effects on the quality-of-life, and correlations with other disorders.
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Affiliation(s)
- Rayan Alshamrani
- Department of Computer Science, University of Idaho, Moscow, ID 83844-1010, USA.,Department of Information Technology, Taif University, Taif, Makkah 26571, Saudi Arabia
| | - Ashrf Althbiti
- Department of Computer Science, University of Idaho, Moscow, ID 83844-1010, USA.,Department of Information Technology, Taif University, Taif, Makkah 26571, Saudi Arabia
| | - Yara Alshamrani
- Department of Information Technology, Taif University, Taif, Makkah 26571, Saudi Arabia.,INTO Program, Washington State University, Pullman, WA 99164-3251, USA
| | - Fatimah Alkomah
- Department of Computer Science, University of Idaho, Moscow, ID 83844-1010, USA.,Department of Information Systems, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Xiaogang Ma
- Department of Computer Science, University of Idaho, Moscow, ID 83844-1010, USA
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Suárez Sánchez A. Ontologías: análisis de sus implementaciones en la bibliotecología. INVESTIGACION BIBLIOTECOLOGICA 2020. [DOI: 10.22201/iibi.24488321xe.2020.83.58135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
El objetivo del artículo es determinar las implementaciones que las ontologías tienen en la bibliotecología. La hipótesis inicial estableció que pueden funcionar como un sistema para la organización del conocimiento (SOC), con funciones similares a los encabezamientos de materia o los tesauros. La metodología empleada consistió en revisión bibliográfica y análisis de casos. Los resultados evidencian que las ontologías en la disciplina bibliotecología pueden implementarse en cinco funciones: 1) representación estructurada de dominios, 2) indización de recursos de información digitales, 3) generación de aprendizaje entre estudiantes y usuarios, 4) construcción de la web semántica, y 5) estructuración de redes de datos enlazados. A partir de los resultados, se confirmó que pueden ser empleadas como un SOC pero, además, pueden ser aplicadas en tareas generales para el modelado de datos o información en la web. Se concluye que son sistemas con alto potencial en la representación y organización de la información en contextos digitales.
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Abstract
My goal in searching for the big pictures is to discover novel ways of organizing information in psychology that will have both theoretical and practical significance. The first section lists my reasons for writing each of five articles. The second section discusses an additional five articles that integrate advancements in artificial intelligence and cognitive psychology. The following two sections elaborate on my collaboration with ontologists to use formal ontologies to organize psychological knowledge, including the National Institute of Mental Health Research Domain Criteria, for formulating a biological basis for mental illness. I next discuss strategies for writing integrative articles. The following section describes the helpfulness of the integrations for making psychology relevant to a general audience. I conclude with recommendations for creating breadth in doctoral training.
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Affiliation(s)
- Stephen K Reed
- Department of Psychology, San Diego State University; Center for Research in Mathematics and Science Education, San Diego State University; and Department of Psychology, University of California, San Diego
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11
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A scoping review of ontologies related to human behaviour change. Nat Hum Behav 2019; 3:164-172. [PMID: 30944444 DOI: 10.1038/s41562-018-0511-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 12/06/2018] [Indexed: 12/16/2022]
Abstract
Ontologies are classification systems specifying entities, definitions and inter-relationships for a given domain, with the potential to advance knowledge about human behaviour change. A scoping review was conducted to: (1) identify what ontologies exist related to human behaviour change, (2) describe the methods used to develop these ontologies and (3) assess the quality of identified ontologies. Using a systematic search, 2,303 papers were identified. Fifteen ontologies met the eligibility criteria for inclusion, developed in areas such as cognition, mental disease and emotions. Methods used for developing the ontologies were expert consultation, data-driven techniques and reuse of terms from existing taxonomies, terminologies and ontologies. Best practices used in ontology development and maintenance were documented. The review did not identify any ontologies representing the breadth and detail of human behaviour change. This suggests that advancing behavioural science would benefit from the development of a behaviour change intervention ontology.
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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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13
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Blanch A, García R, Planes J, Gil R, Balada F, Blanco E, Aluja A. Ontologies About Human Behavior. EUROPEAN PSYCHOLOGIST 2017. [DOI: 10.1027/1016-9040/a000295] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract. The development of information and communication technologies has stimulated a variety of data and informational resources about human behavior. This is contributing toward collaborative efforts in the formalization and systematization of an overwhelming volume of scientific information. Several tools are helpful for this endeavor, among which the ontology is growing in popularity. Most of the available informational resources adopt the ontology to organize a shared conceptualization of a given body of knowledge. In the present study, we reviewed ontology resources (n = 17) that can be of interest to researchers and scholars involved in human behavior and psychological research. The selected ontologies were contrasted on the three main components of ontologies, classes, individuals, and properties, and on scheme and knowledge metrics. Moreover, we recorded the associations of the terms within a given ontology with terms of other ontologies (mappings), the number of projects using a particular ontology, and whether an ontology was available within the Bioportal, an extensive repository about biomedical ontologies. A few working examples were also provided to clarify how these resources might contribute to improve the analysis, understanding, and research cooperation about human behavior and psychological research.
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Affiliation(s)
- Angel Blanch
- Department of Psychology, Faculty of Education, Psychology and Social Work, University of Lleida, Spain
- Institute of Biomedical Research (IRB Lleida), Spain
| | - Roberto García
- Department of Computing Science and Industrial Engineering, University of Lleida, Spain
| | - Jordi Planes
- Department of Computing Science and Industrial Engineering, University of Lleida, Spain
| | - Rosa Gil
- Department of Computing Science and Industrial Engineering, University of Lleida, Spain
| | - Ferran Balada
- Department of Psychobiology, Institute of Neurosciences, Universitat Autònoma de Barcelona, Spain
| | - Eduardo Blanco
- Department of Psychology, Faculty of Education, Psychology and Social Work, University of Lleida, Spain
- Institute of Biomedical Research (IRB Lleida), Spain
| | - Anton Aluja
- Department of Psychology, Faculty of Education, Psychology and Social Work, University of Lleida, Spain
- Institute of Biomedical Research (IRB Lleida), Spain
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Abstract
The Protein Ontology (PRO) is the reference ontology for proteins in the Open Biomedical Ontologies (OBO) foundry and consists of three sub-ontologies representing protein classes of homologous genes, proteoforms (e.g., splice isoforms, sequence variants, and post-translationally modified forms), and protein complexes. PRO defines classes of proteins and protein complexes, both species-specific and species nonspecific, and indicates their relationships in a hierarchical framework, supporting accurate protein annotation at the appropriate level of granularity, analyses of protein conservation across species, and semantic reasoning. In the first section of this chapter, we describe the PRO framework including categories of PRO terms and the relationship of PRO to other ontologies and protein resources. Next, we provide a tutorial about the PRO website ( proconsortium.org ) where users can browse and search the PRO hierarchy, view reports on individual PRO terms, and visualize relationships among PRO terms in a hierarchical table view, a multiple sequence alignment view, and a Cytoscape network view. Finally, we describe several examples illustrating the unique and rich information available in PRO.
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Ettlin DA, Sommer I, Brönnimann B, Maffioletti S, Scheidt J, Hou MY, Lukic N, Steiger B. Design, construction, and technical implementation of a web-based interdisciplinary symptom evaluation (WISE) - a heuristic proposal for orofacial pain and temporomandibular disorders. J Headache Pain 2016; 17:77. [PMID: 27581159 PMCID: PMC5007232 DOI: 10.1186/s10194-016-0670-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 08/17/2016] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Medical symptoms independent of body location burden individuals to varying degrees and may require care by more than one expert. Various paper and computer-based tools exist that aim to comprehensively capture data for optimal clinical management and research. METHODS A web-based interdisciplinary symptom evaluation (WISE) was newly designed, constructed, and technically implemented. For worldwide applicability and to avoid copyright infringements, open source software tools and free validated questionnaires available in multiple languages were used. Highly secure data storage limits access strictly to those who use the tool for collecting, storing, and evaluating their data. Concept and implementation is illustrated by a WISE sample tailored for the requirements of a single center in Switzerland providing interdisciplinary care to orofacial pain and temporomandibular disorder patients. RESULTS By combining a symptom- burden checklist with in-depth questionnaires serving as case-finding instruments, an algorithm was developed that assists in clarifying case complexity and need for targeted expert evaluation. This novel modular approach provides a personalized, response-tailored instrument for the time- and cost-effective collection of symptom-burden focused quantitative data. The tool includes body drawing options and instructional videos. It is applicable for biopsychosocial evaluation in a variety of clinical settings and offers direct feedback by a case report summary. CONCLUSIONS In clinical practice, the new instrument assists in clarifying case complexity and referral need, based on symptom burden and response -tailored case finding. It provides single-case summary reports from a biopsychosocial perspective and includes graphical symptom maps. Secure, centrally stored data collection of anonymous data is possible. The tool enables personalized medicine, facilitates interprofessional education and collaboration, and allows for multicenter patient-reported outcomes research.
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Affiliation(s)
- Dominik A Ettlin
- Orofacial Pain Unit of the Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Isabelle Sommer
- Orofacial Pain Unit of the Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Ben Brönnimann
- Orofacial Pain Unit of the Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Sergio Maffioletti
- S3IT: Service and Support for ScienceIT, University of Zurich, Zurich, Switzerland
| | - Jörg Scheidt
- Institut für Informationssysteme, Hochschule für Angewandte Wissenschaften Hof, Hof, Germany
| | - Mei-Yin Hou
- Orofacial Pain Unit of the Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Nenad Lukic
- Orofacial Pain Unit of the Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Beat Steiger
- Orofacial Pain Unit of the Center of Dental Medicine, University of Zurich, Zurich, Switzerland.
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Diehl AD, Meehan TF, Bradford YM, Brush MH, Dahdul WM, Dougall DS, He Y, Osumi-Sutherland D, Ruttenberg A, Sarntivijai S, Van Slyke CE, Vasilevsky NA, Haendel MA, Blake JA, Mungall CJ. The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability. J Biomed Semantics 2016; 7:44. [PMID: 27377652 PMCID: PMC4932724 DOI: 10.1186/s13326-016-0088-7] [Citation(s) in RCA: 142] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 06/23/2016] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies. CONSTRUCTION AND CONTENT Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning. UTILITY AND DISCUSSION The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs. CONCLUSIONS The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the CL both among developers and within the user community.
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Affiliation(s)
- Alexander D. Diehl
- />Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY 14203 USA
| | - Terrence F. Meehan
- />European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | - Yvonne M. Bradford
- />ZFIN, the Zebrafish Model Organism Database, 5291 University of Oregon, Eugene, OR 97403 USA
| | - Matthew H. Brush
- />Ontology Development Group, Library, Oregon Health and Science University, Portland, Oregon 97239 USA
| | - Wasila M. Dahdul
- />Department of Biology, University of South Dakota, Vermillion, SD 57069 USA
- />National Evolutionary Synthesis Center, Durham, NC 27705 USA
| | - David S. Dougall
- />Southwestern Medical Center, University of Texas, Dallas, TX 75235 USA
| | - Yongqun He
- />Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - David Osumi-Sutherland
- />European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | - Alan Ruttenberg
- />Oral Diagnostics Sciences, University at Buffalo School of Dental Medicine, Buffalo, NY 14210 USA
| | - Sirarat Sarntivijai
- />European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | - Ceri E. Van Slyke
- />ZFIN, the Zebrafish Model Organism Database, 5291 University of Oregon, Eugene, OR 97403 USA
| | - Nicole A. Vasilevsky
- />Ontology Development Group, Library, Oregon Health and Science University, Portland, Oregon 97239 USA
| | - Melissa A. Haendel
- />Ontology Development Group, Library, Oregon Health and Science University, Portland, Oregon 97239 USA
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Castrillo JI, Oliver SG. Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks. Methods Mol Biol 2016; 1303:3-48. [PMID: 26235058 DOI: 10.1007/978-1-4939-2627-5_1] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease (AD), and many neurodegenerative disorders, are multifactorial in nature. They involve a combination of genomic, epigenomic, interactomic and environmental factors. Progress is being made, and these complex diseases are beginning to be understood as having their origin in altered states of biological networks at the cellular level. In the case of AD, genomic susceptibility and mechanisms leading to (or accompanying) the impairment of the central Amyloid Precursor Protein (APP) processing and tau networks are widely accepted as major contributors to the diseased state. The derangement of these networks may result in both the gain and loss of functions, increased generation of toxic species (e.g., toxic soluble oligomers and aggregates) and imbalances, whose effects can propagate to supra-cellular levels. Although well sustained by empirical data and widely accepted, this global perspective often overlooks the essential roles played by the main counteracting homeostatic networks (e.g., protein quality control/proteostasis, unfolded protein response, protein folding chaperone networks, disaggregases, ER-associated degradation/ubiquitin proteasome system, endolysosomal network, autophagy, and other stress-protective and clearance networks), whose relevance to AD is just beginning to be fully realized. In this chapter, an integrative perspective is presented. Alzheimer's disease is characterized to be a result of: (a) intrinsic genomic/epigenomic susceptibility and, (b) a continued dynamic interplay between the deranged networks and the central homeostatic networks of nerve cells. This interplay of networks will underlie both the onset and rate of progression of the disease in each individual. Integrative Systems Biology approaches are required to effect its elucidation. Comprehensive Systems Biology experiments at different 'omics levels in simple model organisms, engineered to recapitulate the basic features of AD may illuminate the onset and sequence of events underlying AD. Indeed, studies of models of AD in simple organisms, differentiated cells in culture and rodents are beginning to offer hope that the onset and progression of AD, if detected at an early stage, may be stopped, delayed, or even reversed, by activating or modulating networks involved in proteostasis and the clearance of toxic species. In practice, the incorporation of next-generation neuroimaging, high-throughput and computational approaches are opening the way towards early diagnosis well before irreversible cell death. Thus, the presence or co-occurrence of: (a) accumulation of toxic Aβ oligomers and tau species; (b) altered splicing and transcriptome patterns; (c) impaired redox, proteostatic, and metabolic networks together with, (d) compromised homeostatic capacities may constitute relevant 'AD hallmarks at the cellular level' towards reliable and early diagnosis. From here, preventive lifestyle changes and tailored therapies may be investigated, such as combined strategies aimed at both lowering the production of toxic species and potentiating homeostatic responses, in order to prevent or delay the onset, and arrest, alleviate, or even reverse the progression of the disease.
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Affiliation(s)
- Juan I Castrillo
- Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge, CB2 1GA, UK,
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Durham J, Raphael KG, Benoliel R, Ceusters W, Michelotti A, Ohrbach R. Perspectives on next steps in classification of oro-facial pain - part 2: role of psychosocial factors. J Oral Rehabil 2015; 42:942-55. [PMID: 26257252 DOI: 10.1111/joor.12329] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/07/2015] [Indexed: 11/30/2022]
Abstract
This study was initiated by a symposium, in which the present authors contributed, organised by the International RDC/TMD Consortium Network in March 2013. The purpose of the study was to review the status of biobehavioural research - both quantitative and qualitative - related to oro-facial pain (OFP) with respect to the aetiology, pathophysiology, diagnosis and management of OFP conditions, and how this information can optimally be used for developing a structured OFP classification system for research. In particular, we address representation of psychosocial entities in classification systems, use of qualitative research to identify and understand the full scope of psychosocial entities and their interaction, and the usage of classification system for guiding treatment. We then provide recommendations for addressing these problems, including how ontological principles can inform this process.
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Affiliation(s)
- J Durham
- Centre for Oral Health Research & Institute of Health & Society, Newcastle University, Newcastle, UK
| | - K G Raphael
- New York University College of Dentistry, New York, NY, USA
| | - R Benoliel
- Rutgers School of Dental Medicine, Newark, NJ, USA
| | | | | | - R Ohrbach
- University at Buffalo, Buffalo, NY, USA
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Hastings J, Frishkoff GA, Smith B, Jensen M, Poldrack RA, Lomax J, Bandrowski A, Imam F, Turner JA, Martone ME. Interdisciplinary perspectives on the development, integration, and application of cognitive ontologies. Front Neuroinform 2014; 8:62. [PMID: 24999329 PMCID: PMC4064452 DOI: 10.3389/fninf.2014.00062] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2013] [Accepted: 06/02/2014] [Indexed: 11/28/2022] Open
Abstract
We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data.
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Affiliation(s)
- Janna Hastings
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus Hinxton, UK ; Department of Philosophy and Swiss Center for Affective Sciences, University of Geneva Switzerland ; Evolutionary Bioinformatics, Swiss Institute for Bioinformatics Lausanne, Switzerland
| | - Gwen A Frishkoff
- Department of Psychology/Neuroscience Institute, Georgia State University GA, USA
| | - Barry Smith
- Department of Philosophy and National Center for Ontological Research, University at Buffalo NY, USA
| | - Mark Jensen
- Department of Philosophy and National Center for Ontological Research, University at Buffalo NY, USA
| | - Russell A Poldrack
- Imaging Research Center, University of Texas at Austin TX, USA ; Department of Psychology, University of Texas at Austin TX, USA ; Department of Neuroscience, University of Texas at Austin TX, USA
| | - Jane Lomax
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus Hinxton, UK
| | - Anita Bandrowski
- Neuroinformatics Framework Project, University of California San Diego, CA, USA
| | - Fahim Imam
- Neuroinformatics Framework Project, University of California San Diego, CA, USA
| | - Jessica A Turner
- Department of Psychology/Neuroscience Institute, Georgia State University GA, USA ; Mind Research Network Albuquerque, NM, USA
| | - Maryann E Martone
- Neuroinformatics Framework Project, University of California San Diego, CA, USA
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