|
1
|
Analysis of evidence appraisals for interventional studies in family medicine using an informatics approach. Prim Health Care Res Dev 2019; 20:e123. [PMID: 31434596 PMCID: PMC6713885 DOI: 10.1017/s1463423619000264] [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] [Indexed: 11/21/2022] Open
Abstract
This study reports the first assessment of published comments in the family medicine literature using structured codes, which produced commentary annotations that will be the foundation of a knowledge base of appraisals of family medicine trials. Evidence appraisal occurs in a variety of formats and serves to shed light on the quality of research. However, scientific discourse generally and evidence appraisal in particular has not itself been analyzed for insights. A search strategy was devised to identify all journal comments indexed in PubMed linked to controlled intervention studies published in a recent 15-year period in major family medicine journals. A previously developed structured representation in the form of a list of appraisal concepts was used to formally annotate and categorize the journal comments through an iterative process. Trends in family medicine evidence appraisal were then analyzed. A total of 93 comments on studies from five journals over 15 years were included in the analysis. Two thirds of extracted appraisals were negative criticisms. All appraisals of measurement instruments were negative (100%). The participants baseline characteristics, the author discussions, and the design of the interventions were also criticized (respectively 91.7%, 84.6% and 83.3% negative). In contrast, appraisals of the scientific basis of the studies were positive (81.8%). The categories with the most appraisals were, most generally, those focused on the study design, and most specifically, those focused on the scientific basis. This study provides a new data-driven approach to review scientific discourse regarding the strengths and limitations of research within academic family medicine. This methodology can potentially generalize to other medical domains. Structured appraisal data generated here will enable future clinical, scientific, and policy decision-making and broader meta-research in family medicine.
Collapse
|
|
2
|
Huang J, Eilbeck K, Smith B, Blake JA, Dou D, Huang W, Natale DA, Ruttenberg A, Huan J, Zimmermann MT, Jiang G, Lin Y, Wu B, Strachan HJ, de Silva N, Kasukurthi MV, Jha VK, He Y, Zhang S, Wang X, Liu Z, Borchert GM, Tan M. The development of non-coding RNA ontology. INT J DATA MIN BIOIN 2016; 15:214-232. [PMID: 27990175 DOI: 10.1504/ijdmb.2016.077072] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Identification of non-coding RNAs (ncRNAs) has been significantly improved over the past decade. On the other hand, semantic annotation of ncRNA data is facing critical challenges due to the lack of a comprehensive ontology to serve as common data elements and data exchange standards in the field. We developed the Non-Coding RNA Ontology (NCRO) to handle this situation. By providing a formally defined ncRNA controlled vocabulary, the NCRO aims to fill a specific and highly needed niche in semantic annotation of large amounts of ncRNA biological and clinical data.
Collapse
Affiliation(s)
- Jingshan Huang
- School of Computing, University of South Alabama, Shelby Hall, Room 1123, 150 Jaguar Drive Mobile, AL 36688, USA,
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA,
| | - Barry Smith
- University at Buffalo - SUNY, Buffalo, New York 14260, USA,
| | | | - Dejing Dou
- Computer and Information Science Department, University of Oregon, Eugene, Oregon 97403, USA,
| | - Weili Huang
- Miracle Query, Inc., Eugene, Oregon 97405, USA,
| | - Darren A Natale
- Georgetown University Medical Center, Washington DC 20007, USA,
| | | | - Jun Huan
- Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, Kansas 66045, USA,
| | - Michael T Zimmermann
- Division of Biomedical Statistics and Informatics, College of Medicine at Mayo Clinic, Rochester, Minnesota 55905, USA,
| | - Guoqian Jiang
- Division of Biomedical Statistics and Informatics, College of Medicine at Mayo Clinic, Rochester, Minnesota 55905, USA,
| | - Yu Lin
- Data Coordination and Integration Center, University of Miami, Miami, Florida 33146, USA,
| | - Bin Wu
- Endocrinology Department, Kunming Medical University, Kunming, Yunnan, 650032 China,
| | - Harrison J Strachan
- School of Computing, University of South Alabama, Mobile, Alabama 36688, USA,
| | - Nisansa de Silva
- Computer and Information Science, University of Oregon, Eugene, Oregon 97403, USA,
| | | | - Vikash Kumar Jha
- School of Computing, University of South Alabama, Mobile, Alabama 36688, USA,
| | - Yongqun He
- Lab Animal Medicine, Microbiology, Immunology and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA,
| | - Shaojie Zhang
- Department of Computer Science, University of Central Florida, Orlando, Florida 32816, USA,
| | - Xiaowei Wang
- Cancer Biology, Washington University in St. Louis, St. Louis, Missouri 63130, USA,
| | - Zixing Liu
- Mitchell Cancer Institute, University of South Alabama, Mobile, Alabama 36604, USA,
| | - Glen M Borchert
- Department of Biology, University of South Alabama, Mobile, Alabama 36688, USA,
| | - Ming Tan
- Mitchell Cancer Institute, University of South Alabama, Mobile, Alabama 36604, USA,
| |
Collapse
|
|
3
|
The Non-Coding RNA Ontology (NCRO): a comprehensive resource for the unification of non-coding RNA biology. J Biomed Semantics 2016; 7:24. [PMID: 27152146 PMCID: PMC4857245 DOI: 10.1186/s13326-016-0066-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Accepted: 04/19/2016] [Indexed: 11/17/2022] Open
Abstract
In recent years, sequencing technologies have enabled the identification of a wide range of non-coding RNAs (ncRNAs). Unfortunately, annotation and integration of ncRNA data has lagged behind their identification. Given the large quantity of information being obtained in this area, there emerges an urgent need to integrate what is being discovered by a broad range of relevant communities. To this end, the Non-Coding RNA Ontology (NCRO) is being developed to provide a systematically structured and precisely defined controlled vocabulary for the domain of ncRNAs, thereby facilitating the discovery, curation, analysis, exchange, and reasoning of data about structures of ncRNAs, their molecular and cellular functions, and their impacts upon phenotypes. The goal of NCRO is to serve as a common resource for annotations of diverse research in a way that will significantly enhance integrative and comparative analysis of the myriad resources currently housed in disparate sources. It is our belief that the NCRO ontology can perform an important role in the comprehensive unification of ncRNA biology and, indeed, fill a critical gap in both the Open Biological and Biomedical Ontologies (OBO) Library and the National Center for Biomedical Ontology (NCBO) BioPortal. Our initial focus is on the ontological representation of small regulatory ncRNAs, which we see as the first step in providing a resource for the annotation of data about all forms of ncRNAs. The NCRO ontology is free and open to all users, accessible at: http://purl.obolibrary.org/obo/ncro.owl.
Collapse
|