*informatics: Identifying and Tracking Informatics Sub-Discipline Terms in the Literature.

OBJECTIVE: To identify the breadth of informatics sub-discipline terms used in the literature for enabling subsequent organization and searching by sub-discipline. METHODS: Titles in five literature sources were analyzed to extract terms for informatics sub-disciplines: 1) United States (U.S.) Library of Congress Online Catalog, 2) English Wikipedia, 3) U.S. National Library of Medicine (NLM) Catalog, 4) PubMed, and 5) PubMed Central. The extracted terms were combined and standardized with those in four vocabulary sources to create an integrated list: 1) Library of Congress Subject Headings (LCSH), 2) Medical Subject Headings (MeSH), 3) U.S. National Cancer Institute Thesaurus (NCIt), and 4) EMBRACE Data and Methods (EDAM). Searches for terms in titles from each literature source were conducted to obtain frequency counts and start years for characterizing established and potentially emerging sub-disciplines. RESULTS: Analysis of 6,949 titles from literature sources and 67 terms from vocabulary sources resulted in an integrated list of 382 terms for informatics sub-disciplines mapped to 292 preferred terms. In the last five decades, "bioinformatics", "medical informatics", "health informatics", "nursing informatics", and "biomedical informatics" were associated with the most literature. In the current decade, potentially emerging sub-disciplines include "disability informatics", "neonatal informatics", and "nanoinformatics" based on literature from the last five years. CONCLUSIONS: As the field of informatics continues to expand and advance, keeping up-to-date with historical and current trends will become increasingly challenging. The ability to track the accomplishments and evolution of a particular sub-discipline in the literature could be valuable for supporting informatics research, education, and training.

Managing interoperability and complexity in health systems.

In recent years, we have witnessed substantial progress in the use of clinical informatics systems to support clinicians during episodes of care, manage specialised domain knowledge, perform complex clinical data analysis and improve the management of health organisations' resources. However, the vision of fully integrated health information eco-systems, which provide relevant information and useful knowledge at the point-of-care, remains elusive. This journal Focus Theme reviews some of the enduring challenges of interoperability and complexity in clinical informatics systems. Furthermore, a range of approaches are proposed in order to address, harness and resolve some of the many remaining issues towards a greater integration of health information systems and extraction of useful or new knowledge from heterogeneous electronic data repositories.

Bringing genome tests into clinical practice.

OBJECTIVE: To provide a review of recent progress in the use of genomic data in clinical contexts. METHOD: Survey of key articles selected from the previous year (2012). RESULT: Two major themes emerged as areas of focus in 2012: (1) integration of genomic data into electronic health records; and (2) determining the clinical utility of genomic tests. CONCLUSION: Significant advances are being made towards the integration of genomic data such that they may useful for clinical decision making. While the short term advances will likely still be seen in clinically valid genotype-phenotype research, there have been promising advances towards developing genomic tests that may be of clinical utility in the near future.

Evaluation of biomedical informatics innovations and their impact on public health.

This issue of Methods of Information in Medicine contains four feature articles that are focused on the theme of evaluation. Evaluation approaches are increasingly essential in the assessment of determining the potential impact of contemporary informatics innovations. The featured articles offer practical perspectives to determining the impact of advancements. Internationally, there are significant advances being made across biomedical informatics and its related sub-disciplines. As with any scientific discipline, it is important for practitioners to be able to relate the potential importance of findings. To this end, it is especially important for biomedical informaticians to convey, in a quantifiable and comparable form, the significance of the informatics findings -not only to peers but also to those across the biomedical research spectrum. As such, the feature articles in this issue describe the evaluation of core infrastructure and fundamental informatics innovations as well as evaluation of informatics-based resources that are a core aspect of public health initiatives.

The birth and evolution of a discipline devoted to information in biomedicine and health care. As reflected in its longest running journal.

BACKGROUND: The journal Methods of Information in Medicine, founded in 1962, has now completed its 50th volume. Its publications during the last five decades reflect the formation of a discipline that deals with information in biomedicine and health care. OBJECTIVES: To report about 1) the journal's origin, 2) the individuals who have significantly contributed to it, 3) trends in the journal's aims and scope, 4) influential papers and 5) major topics published in Methods over the years. METHODS: Methods included analysing the correspondence and journal issues in the archives of the editorial office and of the publisher, citation analysis using the ISI and Scopus databases, and analysing the articles' Medical Subject Headings (MeSH) in MEDLINE. RESULTS: In the journal's first 50 years 208 editorial board members and/or editors contributed to the journal's development, with most individuals coming from Europe and North America. The median time of service was 11 years. At the time of analysis 2,456 articles had been indexed with MeSH. Topics included computerized systems of various types, informatics methodologies, and topics related to a specific medical domain. Some MeSH topic entries were heavily and regularly represented in each of the journal's five decades (e.g. information systems and medical records), while others were important in a particular decade, but not in other decades (e.g. punched-card systems and systems integration). Seven papers were cited more than 100 times and these also covered a broad range of themes such as knowledge representation, analysis of biomedical data and knowledge, clinical decision support and electronic patient records. CONCLUSIONS: Methods of Information in Medicine is the oldest international journal in biomedical informatics. The journal's development over the last 50 years correlates with the formation of this new discipline. It has and continues to stress the basic methodology and scientific fundamentals of organizing, representing and analysing data, information and knowledge in biomedicine and health care. It has and continues to stimulate multidisciplinary communication on research that is devoted to high-quality, efficient health care, to quality of life and to the progress of biomedicine and the health sciences.

Biomedical informatics--a confluence of disciplines?

BACKGROUND: Biomedical informatics is a broad discipline that borrows many methods and techniques from other disciplines. OBJECTIVE: To reflect a) on the character of biomedical informatics and to determine whether it is multi-disciplinary or inter-disciplinary; b) on the question whether biomedical informatics is more than the sum of its supporting disciplines and c) on the position of biomedical informatics with respect to related disciplines. METHOD: Inviting an international group of experts in biomedical informatics and related disciplines on the occasion of the 50th anniversary of Methods of Information in Medicine to present their viewpoints. RESULTS AND CONCLUSIONS: This paper contains the reflections of a number of the invited experts on the character of biomedical informatics. Most of the authors agree that biomedical informatics is an interdisciplinary field of study where researchers with different scientific backgrounds alone or in combination carry out research. Biomedical informatics is a very broad scientific field and still expanding, yet comprised of a constructive aspect (designing and building systems). One author expressed that the essence of biomedical informatics, as opposed to related disciplines, lies in the modelling of the biomedical content. Interdisciplinarity also has consequences for education. Maintaining rigid disciplinary structures does not allow for sufficient adaptability to capitalize on important trends nor to leverage the influences these trends may have on biomedical informatics. It is therefore important for students to become aware of research findings in related disciplines. In this respect, it was also noted that the fact that many scientific fields use different languages and that the research findings are stored in separate bibliographic databases makes it possible that potentially connected findings will never be linked, despite the fact that these findings were published. Bridges between the sciences are needed for the success of biomedical informatics.

Data analysis and data mining: current issues in biomedical informatics.

BACKGROUND: Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research. OBJECTIVES: To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. METHODS: On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, which reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field. RESULTS: The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology. CONCLUSIONS: Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.

Character-based DNA barcoding allows discrimination of genera, species and populations in Odonata.

DNA barcoding has become a promising means for identifying organisms of all life stages. Currently, phenetic approaches and tree-building methods have been used to define species boundaries and discover 'cryptic species'. However, a universal threshold of genetic distance values to distinguish taxonomic groups cannot be determined. As an alternative, DNA barcoding approaches can be 'character based', whereby species are identified through the presence or absence of discrete nucleotide substitutions (character states) within a DNA sequence. We demonstrate the potential of character-based DNA barcodes by analysing 833 odonate specimens from 103 localities belonging to 64 species. A total of 54 species and 22 genera could be discriminated reliably through unique combinations of character states within only one mitochondrial gene region (NADH dehydrogenase 1). Character-based DNA barcodes were further successfully established at a population level discriminating seven population-specific entities out of a total of 19 populations belonging to three species. Thus, for the first time, DNA barcodes have been found to identify entities below the species level that may constitute separate conservation units or even species units. Our findings suggest that character-based DNA barcoding can be a rapid and reliable means for (i) the assignment of unknown specimens to a taxonomic group, (ii) the exploration of diagnosability of conservation units, and (iii) complementing taxonomic identification systems.

An evaluation of hybrid methods for matching biomedical terminologies: mapping the gene ontology to the UMLS.

Integration of disparate biomedical terminologies is becoming increasingly important as links between biological science and clinical medicine grow. Mapping concepts in the Gene Ontology (GO) to the UMLS may help further this integration and allow for more efficient information exchange among researchers. Using a gold standard of GO term--UMLS concept mappings provided by the NCI, we examined the performance of various published and combined mapping techniques, in order to maximize precision and recall. We found that for the previously published techniques precision varied between (0.61-0.95), and recall varied from (0.65-0.90), whereas for the hybrid techniques, precision varied between (0.66-0.97), and recall from (0.59-0.93). Our study reveals the benefits of using mapping techniques that incorporate domain knowledge, and provides a basis for future approaches to mapping between distinct biomedical vocabularies.

Linking biomedical language information and knowledge resources: GO and UMLS.

Integration of various informatics terminologies will be an essential activity towards supporting the advancement of both the biomedical and clinical sciences. The GO consortium has developed an impressive collection of biomedical terms specific to genes and proteins in a variety of organisms. The UMLS is a composite collection of various medical terminologies, pioneered by the National Library of Medicine. In the present study, we examine a variety of techniques for mapping terms from one terminology (GO) to another (UMLS), and describe their respective performances for a small, curated data set attained from the National Cancer Institute, which had precision values ranging from 30% (100% recall) to 95% (74% recall). Based on each technique's performance, we comment on how each can be used to enrich an existing terminology (UMLS) in future studies and how linking biological terminologies to UMLS differs from linking medical terminologies.

Characteristic attributes in cancer microarrays.

Rapid advances in genome sequencing and gene expression microarray technologies are providing unprecedented opportunities to identify specific genes involved in complex biological processes, such as development, signal transduction, and disease. The vast amount of data generated by these technologies has presented new challenges in bioinformatics. To help organize and interpret microarray data, new and efficient computational methods are needed to: (1) distinguish accurately between different biological or clinical categories (e.g., malignant vs. benign), and (2) identify specific genes that play a role in determining those categories. Here we present a novel and simple method that exhaustively scans microarray data for unambiguous gene expression patterns. Such patterns of data can be used as the basis for classification into biological or clinical categories. The method, termed the Characteristic Attribute Organization System (CAOS), is derived from fundamental precepts in systematic biology. In CAOS we define two types of characteristic attributes ('pure' and 'private') that may exist in gene expression microarray data. We also consider additional attributes ('compound') that are composed of expression states of more than one gene that are not characteristic on their own. CAOS was tested on three well-known cancer DNA microarray data sets for its ability to classify new microarray samples. We found CAOS to be a highly accurate and robust class prediction technique. In addition, CAOS identified specific genes, not emphasized in other analyses, that may be crucial to the biology of certain types of cancer. The success of CAOS in this study has significant implications for basic research and the future development of reliable methods for clinical diagnostic tools.