Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care

Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry

With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.

Biomedical Vibrational Spectroscopy in the Era of Artificial Intelligence

Biomedical vibrational spectroscopy has come of age. The past twenty years have brought many advancements and new developments and now its practitioners face a new challenge: artificial intelligence. Artificial intelligence has the capability to detect meaningful relationships in data sets such as those found in an infrared or Raman spectrum. The present narrative assesses the degree to which biomedical vibrational spectroscopy has already embraced artificial intelligence and what can be expected going forward. This article belongs to the Special Issue Biomedical Applications of Infrared and Raman Spectroscopy.

Bioinformatics in Mexico: A diagnostic from the academic perspective and recommendations for a public policy

In this work, we present a diagnostic analysis of strengths, weaknesses, opportunities and threats (SWOT) of the current state of Bioinformatics in Mexico. We conducted semi-structured interviews among researchers and academics with key expertise in this field, identified by bibliometric analyses and qualitative sampling techniques. Additionally, an online survey was conducted reaching a higher number of respondents. Among the relevant findings of our study, the lack of specialized human resources and technological infrastructure stood out, along with deficiencies in the number and quality of academic programs, scarce public investment and a weak relationship between public and private institutions. However, there are great opportunities for developing a national Bioinformatics to support different economic sectors. In our opinion, this work could be useful to favor a comprehensive network among Mexican researchers, in order to lay the foundations of a national strategy towards a well designed public policy.

Cross-disciplinary evolution of the genomics revolution

Born out of the Human Genome Project (HGP), the field of genomics evolved with phenomenal speed into a dominant scientific and business force. While other efforts were intent on estimating the economic impact of the genomics revolution, we shift focus to the social and cultural capital generated by bridging together biology and computing-two of the constitutive disciplines of "genomics". We quantify this capital by measuring the pervasiveness of bio-computing cross-disciplinarity (XD) in genomics research during and after the HGP. To provide interlocking perspectives at the career and epistemic levels, we assembled three data sets to measure XD via (i) the collaboration network between 4190 biology and computing faculty from 155 departments in the United States, (ii) cross-departmental affiliations within a comprehensive set of human genomics publications, and (iii) the application of computational concepts and methods in research published in a preeminent genomics journal. Our results show the following: First, research featuring XD collaborations has higher citation impact than other disciplinary research-an effect observed at both the career and individual article levels. Second, genomics articles featuring XD methods tend to have higher citation impact than epistemically pure articles. Third, XD researchers of computing pedigree are drawn to the biology culture. This statistical evidence acquires deeper meaning when viewed against the organizational and knowledge transfer mechanisms revealed by the data models. With cross-disciplinary initiatives set to dominate the agenda of funding agencies, our case study provides a framework for appreciating the long-term effects of these initiatives on science and its standard-bearers.

Discussion of "The New Role of Biomedical Informatics in the Age of Digital Medicine"

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "The New Role of Biomedical Informatics in the Age of Digital Medicine" written by Fernando J. Martin-Sanchez and Guillermo H. Lopez-Campos [1]. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of Martin-Sanchez and Lopez-Campos. In subsequent issues the discussion can continue through letters to the editor.

Taking Bioinformatics to Systems Medicine

Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.

On the security flaws in ID-based password authentication schemes for telecare medical information systems

Telecare medical information systems (TMIS) enable healthcare delivery services. However, access of these services via public channel raises security and privacy issues. In recent years, several smart card based authentication schemes have been introduced to ensure secure and authorized communication between remote entities over the public channel for the (TMIS). We analyze the security of some of the recently proposed authentication schemes of Lin, Xie et al., Cao and Zhai, and Wu and Xu's for TMIS. Unfortunately, we identify that these schemes failed to satisfy desirable security attributes. In this article we briefly discuss four dynamic ID-based authentication schemes and demonstrate their failure to satisfy desirable security attributes. The study is aimed to demonstrate how inefficient password change phase can lead to denial of server scenario for an authorized user, and how an inefficient login phase causes the communication and computational overhead and decrease the performance of the system. Moreover, we show the vulnerability of Cao and Zhai's scheme to known session specific temporary information attack, vulnerability of Wu and Xu's scheme to off-line password guessing attack, and vulnerability of Xie et al.'s scheme to untraceable on-line password guessing attack.

Dry computational approaches for wet medical problems

This is a report on the 4th international conference in 'Quantitative Biology and Bioinformatics in Modern Medicine' held in Belfast (UK), 19-20 September 2013. The aim of the conference was to bring together leading experts from a variety of different areas that are key for Systems Medicine to exchange novel findings and promote interdisciplinary ideas and collaborations.

Stakeholder engagement: a key component of integrating genomic information into electronic health records

Integrating genomic information into clinical care and the electronic health record can facilitate personalized medicine through genetically guided clinical decision support. Stakeholder involvement is critical to the success of these implementation efforts. Prior work on implementation of clinical information systems provides broad guidance to inform effective engagement strategies. We add to this evidence-based recommendations that are specific to issues at the intersection of genomics and the electronic health record. We describe stakeholder engagement strategies employed by the Electronic Medical Records and Genomics Network, a national consortium of US research institutions funded by the National Human Genome Research Institute to develop, disseminate, and apply approaches that combine genomic and electronic health record data. Through select examples drawn from sites of the Electronic Medical Records and Genomics Network, we illustrate a continuum of engagement strategies to inform genomic integration into commercial and homegrown electronic health records across a range of health-care settings. We frame engagement as activities to consult, involve, and partner with key stakeholder groups throughout specific phases of health information technology implementation. Our aim is to provide insights into engagement strategies to guide genomic integration based on our unique network experiences and lessons learned within the broader context of implementation research in biomedical informatics. On the basis of our collective experience, we describe key stakeholder practices, challenges, and considerations for successful genomic integration to support personalized medicine.

Micro-Analyzer: automatic preprocessing of Affymetrix microarray data

A current trend in genomics is the investigation of the cell mechanism using different technologies, in order to explain the relationship among genes, molecular processes and diseases. For instance, the combined use of gene-expression arrays and genomic arrays has been demonstrated as an effective instrument in clinical practice. Consequently, in a single experiment different kind of microarrays may be used, resulting in the production of different types of binary data (images and textual raw data). The analysis of microarray data requires an initial preprocessing phase, that makes raw data suitable for use on existing analysis platforms, such as the TIGR M4 (TM4) Suite. An additional challenge to be faced by emerging data analysis platforms is the ability to treat in a combined way those different microarray formats coupled with clinical data. In fact, resulting integrated data may include both numerical and symbolic data (e.g. gene expression and SNPs regarding molecular data), as well as temporal data (e.g. the response to a drug, time to progression and survival rate), regarding clinical data. Raw data preprocessing is a crucial step in analysis but is often performed in a manual and error prone way using different software tools. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of different microarray data are needed. The paper presents Micro-Analyzer (Microarray Analyzer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix gene expression and SNP binary data. It represents the evolution of the μ-CS tool, extending the preprocessing to SNP arrays that were not allowed in μ-CS. The Micro-Analyzer is provided as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data (gene expression and SNPs) by invoking TM4 platform. It avoids: (i) the manual invocation of external tools (e.g. the Affymetrix Power Tools), (ii) the manual loading of preprocessing libraries, and (iii) the management of intermediate files, such as results and metadata. Micro-Analyzer users can directly manage Affymetrix binary data without worrying about locating and invoking the proper preprocessing tools and chip-specific libraries. Moreover, users of the Micro-Analyzer tool can load the preprocessed data directly into the well-known TM4 platform, extending in such a way also the TM4 capabilities. Consequently, Micro Analyzer offers the following advantages: (i) it reduces possible errors in the preprocessing and further analysis phases, e.g. due to the incorrect choice of parameters or due to the use of old libraries, (ii) it enables the combined and centralized pre-processing of different arrays, (iii) it may enhance the quality of further analysis by storing the workflow, i.e. information about the preprocessing steps, and (iv) finally Micro-Analzyer is freely available as a standalone application at the project web site http://sourceforge.net/projects/microanalyzer/.

Genetic counselors' current use of personal health records-based family histories in genetic clinics and considerations for their future adoption

Given the widespread adoption of electronic medical records and recent emergence of electronic family history tools, we examined genetic counselors' perspectives on the emerging technology of the personal health record (PHR)-based family history tool that links to an electronic medical record (EMR). Two-hundred thirty-three genetic counselors responded to an on-line survey eliciting current use of electronic family history (EFH) tools and familiarity with PHR-based family history tools. Additionally, after being shown a series of screen shots of a newly developed PHR-based family history tool based on the U.S. Surgeon General's My Family Health Portrait (United States Department of Health and Human Services 2009), participants were surveyed about the perceived usefulness, ease of use, and impact on current workflow that this kind of tool would have in their practices. Eighty-three percent reported that their institution has an EMR, yet only 35 % have a dedicated space for family history. Eighty-two percent reported that less than 5 % of their patients have a PHR, and only 16 % have worked with patients who have a PHR. Seventy-two percent or more agreed that a PHR-based family history tool would facilitate communication, increase accuracy of information, ensure consistency in recording information, increase focus on actual counseling, reduce repetitive questions, improve efficiency, and increase the legibility and clarity. Our findings suggest that participants were familiar with existing EFH tools, but that the majority did not use them in practice. Genetic counselors' adoption of such tools is limited due to non-existence of this kind of technology or inability to integrate it into their clinics. They are also strongly in favor of adopting a PHR-based family history tool in genetics clinics, but have practical concerns that must be addressed before the tool can be implemented.

DMET-analyzer: automatic analysis of Affymetrix DMET data

Background

Clinical Bioinformatics is currently growing and is based on the integration of clinical and omics data aiming at the development of personalized medicine. Thus the introduction of novel technologies able to investigate the relationship among clinical states and biological machineries may help the development of this field. For instance the Affymetrix DMET platform (drug metabolism enzymes and transporters) is able to study the relationship among the variation of the genome of patients and drug metabolism, detecting SNPs (Single Nucleotide Polymorphism) on genes related to drug metabolism. This may allow for instance to find genetic variants in patients which present different drug responses, in pharmacogenomics and clinical studies. Despite this, there is currently a lack in the development of open-source algorithms and tools for the analysis of DMET data. Existing software tools for DMET data generally allow only the preprocessing of binary data (e.g. the DMET-Console provided by Affymetrix) and simple data analysis operations, but do not allow to test the association of the presence of SNPs with the response to drugs.

Results

We developed DMET-Analyzer a tool for the automatic association analysis among the variation of the patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The proposed system allows: (i) to automatize the workflow of analysis of DMET-SNP data avoiding the use of multiple tools; (ii) the automatic annotation of DMET-SNP data and the search in existing databases of SNPs (e.g. dbSNP), (iii) the association of SNP with pathway through the search in PharmaGKB, a major knowledge base for pharmacogenomic studies. DMET-Analyzer has a simple graphical user interface that allows users (doctors/biologists) to upload and analyse DMET files produced by Affymetrix DMET-Console in an interactive way. The effectiveness and easy use of DMET Analyzer is demonstrated through different case studies regarding the analysis of clinical datasets produced in the University Hospital of Catanzaro, Italy.

Conclusion

DMET Analyzer is a novel tool able to automatically analyse data produced by the DMET-platform in case-control association studies. Using such tool user may avoid wasting time in the manual execution of multiple statistical tests avoiding possible errors and reducing the amount of time needed for a whole experiment. Moreover annotations and the direct link to external databases may increase the biological knowledge extracted. The system is freely available for academic purposes at: https://sourceforge.net/projects/dmetanalyzer/files/

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.

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended "nanotype" to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others.

A Dimensional Bus model for integrating clinical and research data

OBJECTIVES

Many clinical research data integration platforms rely on the Entity-Attribute-Value model because of its flexibility, even though it presents problems in query formulation and execution time. The authors sought more balance in these traits.

MATERIALS AND METHODS

Borrowing concepts from Entity-Attribute-Value and from enterprise data warehousing, the authors designed an alternative called the Dimensional Bus model and used it to integrate electronic medical record, sponsored study, and biorepository data. Each type of observational collection has its own table, and the structure of these tables varies to suit the source data. The observational tables are linked to the Bus, which holds provenance information and links to various classificatory dimensions that amplify the meaning of the data or facilitate its query and exposure management.

RESULTS

The authors implemented a Bus-based clinical research data repository with a query system that flexibly manages data access and confidentiality, facilitates catalog search, and readily formulates and compiles complex queries.

CONCLUSION

The design provides a workable way to manage and query mixed schemas in a data warehouse.

Translational bioinformatics: linking knowledge across biological and clinical realms

Nearly a decade since the completion of the first draft of the human genome, the biomedical community is positioned to usher in a new era of scientific inquiry that links fundamental biological insights with clinical knowledge. Accordingly, holistic approaches are needed to develop and assess hypotheses that incorporate genotypic, phenotypic, and environmental knowledge. This perspective presents translational bioinformatics as a discipline that builds on the successes of bioinformatics and health informatics for the study of complex diseases. The early successes of translational bioinformatics are indicative of the potential to achieve the promise of the Human Genome Project for gaining deeper insights to the genetic underpinnings of disease and progress toward the development of a new generation of therapies.

A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems

The objective of this review is to describe the implementation of human factors principles for the design of alerts in clinical information systems. First, we conduct a review of alarm systems to identify human factors principles that are employed in the design and implementation of alerts. Second, we review the medical informatics literature to provide examples of the implementation of human factors principles in current clinical information systems using alerts to provide medication decision support. Last, we suggest actionable recommendations for delivering effective clinical decision support using alerts. A review of studies from the medical informatics literature suggests that many basic human factors principles are not followed, possibly contributing to the lack of acceptance of alerts in clinical information systems. We evaluate the limitations of current alerting philosophies and provide recommendations for improving acceptance of alerts by incorporating human factors principles in their design.

Phenotypic and genotypic data integration and exploration through a web-service architecture

Background

Linking genotypic and phenotypic information is one of the greatest challenges of current genetics research. The definition of an Information Technology infrastructure to support this kind of studies, and in particular studies aimed at the analysis of complex traits, which require the definition of multifaceted phenotypes and the integration genotypic information to discover the most prevalent diseases, is a paradigmatic goal of Biomedical Informatics. This paper describes the use of Information Technology methods and tools to develop a system for the management, inspection and integration of phenotypic and genotypic data.

Results

We present the design and architecture of the Phenotype Miner, a software system able to flexibly manage phenotypic information, and its extended functionalities to retrieve genotype information from external repositories and to relate it to phenotypic data. For this purpose we developed a module to allow customized data upload by the user and a SOAP-based communications layer to retrieve data from existing biomedical knowledge management tools. In this paper we also demonstrate the system functionality by an example application of the system in which we analyze two related genomic datasets.

Conclusion

In this paper we show how a comprehensive, integrated and automated workbench for genotype and phenotype integration can facilitate and improve the hypothesis generation process underlying modern genetic studies.

Training multidisciplinary biomedical informatics students: three years of experience

OBJECTIVE

The European INFOBIOMED Network of Excellence recognized that a successful education program in biomedical informatics should include not only traditional teaching activities in the basic sciences but also the development of skills for working in multidisciplinary teams.

DESIGN

A carefully developed 3-year training program for biomedical informatics students addressed these educational aspects through the following four activities: (1) an internet course database containing an overview of all Medical Informatics and BioInformatics courses, (2) a BioMedical Informatics Summer School, (3) a mobility program based on a 'brokerage service' which published demands and offers, including funding for research exchange projects, and (4) training challenges aimed at the development of multi-disciplinary skills.

MEASUREMENTS

This paper focuses on experiences gained in the development of novel educational activities addressing work in multidisciplinary teams. The training challenges described here were evaluated by asking participants to fill out forms with Likert scale based questions. For the mobility program a needs assessment was carried out.

RESULTS

The mobility program supported 20 exchanges which fostered new BMI research, resulted in a number of peer-reviewed publications and demonstrated the feasibility of this multidisciplinary BMI approach within the European Union. Students unanimously indicated that the training challenge experience had contributed to their understanding and appreciation of multidisciplinary teamwork.

CONCLUSION

The training activities undertaken in INFOBIOMED have contributed to a multi-disciplinary BMI approach. It is our hope that this work might provide an impetus for training efforts in Europe, and yield a new generation of biomedical informaticians.

A perspective for biomedical data integration: design of databases for flow cytometry

Background

The integration of biomedical information is essential for tackling medical problems. We describe a data model in the domain of flow cytometry (FC) allowing for massive management, analysis and integration with other laboratory and clinical information. The paper is concerned with the proper translation of the Flow Cytometry Standard (FCS) into a relational database schema, in a way that facilitates end users at either doing research on FC or studying specific cases of patients undergone FC analysis

Results

The proposed database schema provides integration of data originating from diverse acquisition settings, organized in a way that allows syntactically simple queries that provide results significantly faster than the conventional implementations of the FCS standard. The proposed schema can potentially achieve up to 8 orders of magnitude reduction in query complexity and up to 2 orders of magnitude reduction in response time for data originating from flow cytometers that record 256 colours. This is mainly achieved by managing to maintain an almost constant number of data-mining procedures regardless of the size and complexity of the stored information.

Conclusion

It is evident that using single-file data storage standards for the design of databases without any structural transformations significantly limits the flexibility of databases. Analysis of the requirements of a specific domain for integration and massive data processing can provide the necessary schema modifications that will unlock the additional functionality of a relational database.

Medical informatics and bioinformatics: a bibliometric study

This paper reports on an analysis of the bioinformatics and medical informatics literature with the objective to identify upcoming trends that are shared among both research fields to derive benefits from potential collaborative initiatives for their future. Our results present the main characteristics of the two fields and show that these domains are still relatively separated.

Non-linear correlation of content and metadata information extracted from biomedical article datasets

Biomedical literature databases constitute valuable repositories of up to date scientific knowledge. The development of efficient machine learning methods in order to facilitate the organization of these databases and the extraction of novel biomedical knowledge is becoming increasingly important. Several of these methods require the representation of the documents as vectors of variables forming large multivariate datasets. Since the amount of information contained in different datasets is voluminous, an open issue is to combine information gained from various sources to a concise new dataset, which will efficiently represent the corpus of documents. This paper investigates the use of the multivariate statistical approach, called Non-Linear Canonical Correlation Analysis (NLCCA), for exploiting the correlation among the variables of different document representations and describing the documents with only one new dataset. Experiments with document datasets represented by text words, Medical Subject Headings (MeSH) and Gene Ontology (GO) terms showed the effectiveness of NLCCA.

A longitudinal social network analysis of the editorial boards of medical informatics and bioinformatics journals

OBJECTIVE

The goal of this research is to learn how the editorial staffs of bioinformatics and medical informatics journals provide support for cross-community exposure. Models such as co-citation and co-author analysis measure the relationships between researchers; but they do not capture how environments that support knowledge transfer across communities are organized.

METHODS

In this paper, we propose a social network analysis model to study how editorial boards integrate researchers from disparate communities. We evaluate our model by building relational networks based on the editorial boards of approximately 40 journals that serve as research outlets in medical informatics and bioinformatics. We track the evolution of editorial relationships through a longitudinal investigation over the years 2000 through 2005.

RESULTS

Our findings suggest that there are research journals that support the collocation of editorial board members from the bioinformatics and medical informatics communities. Network centrality metrics indicate that editorial board members are located in the intersection of the communities and that the number of individuals in the intersection is growing with time.

CONCLUSIONS

Social network analysis methods provide insight into the relationships between the medical informatics and bioinformatics communities. The number of editorial board members facilitating the publication intersection of the communities has grown, but the intersection remains dependent on a small group of individuals and fragile.

SYMBIOmatics: synergies in Medical Informatics and Bioinformatics--exploring current scientific literature for emerging topics

Background

The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to ). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics.

Results

This paper presents results of a systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000–2005 ("recent") and 1990–1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays.

Conclusion

We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science.

Systematic planning of patient records for cooperative care and multicenter research

PURPOSE

The purpose of this paper is to introduce a method for systematically planning patient records for structured data entry that can be used in cooperative environments (e.g. cooperative care, multicenter trials) in a way that enables multipurpose use and shared data entry.

METHODS

Design research, formal logic.

RESULTS

The method suggests five steps: analyze the prevailing documentation infrastructure, provide terminology management system (TMS), provide documentation management system (DMS), plan the logical architecture, provide all necessary tools.

CONCLUSIONS

The era of eHealth enables cooperative care and collaborative documentation. This can only be efficient if a multiple use and shared entry of data is realized. The task of the medical informatics community is to plan these environments systematically especially in complex environments which are enabled by emerging technologies.

Bridging the gap between biological and clinical informatics in a graduate training program

Several training programs in biomedical informatics in the United States are attempting to integrate biological and clinical informatics. However, significant differences in the cultures underlying these two disciplines pose barriers to a uniform educational solution. This paper recounts the experience at Columbia University in adapting a graduate program with an initial focus on clinical informatics to train bioinformaticians. The analysis begins by considering the development of the medical and biological informatics cultures over a 17-year period. Then we review how two separate curricula evolved to serve the needs of each group. Interviews with bioinformatics students and faculty indicated some dissatisfaction with the curriculum that developed within clinical informatics. Their comments are considered in the light of an analysis of the relationship between the application domains of biomedical informatics as a discipline. In response, a new curriculum was developed in which bioinformatics and clinical informatics are regarded as subdivisions of the same subject. A key feature of this curriculum is a new course, Theory and Methods in Biomedical Informatics, which presents informatics principles in their general form, and illustrates their application with examples drawn from across the biomedical spectrum. The paper concludes with suggestions for integrating informatics training programs at other institutions.

Data integration and genomic medicine

Genomic medicine aims to revolutionize health care by applying our growing understanding of the molecular basis of disease. Research in this arena is data intensive, which means data sets are large and highly heterogeneous. To create knowledge from data, researchers must integrate these large and diverse data sets. This presents daunting informatic challenges such as representation of data that is suitable for computational inference (knowledge representation), and linking heterogeneous data sets (data integration). Fortunately, many of these challenges can be classified as data integration problems, and technologies exist in the area of data integration that may be applied to these challenges. In this paper, we discuss the opportunities of genomic medicine as well as identify the informatics challenges in this domain. We also review concepts and methodologies in the field of data integration. These data integration concepts and methodologies are then aligned with informatics challenges in genomic medicine and presented as potential solutions. We conclude this paper with challenges still not addressed in genomic medicine and gaps that remain in data integration research to facilitate genomic medicine.

Bridging the gap between medical and bioinformatics: an ontological case study in colon carcinoma

Ontological principles are needed in order to bridge the gap between medical and biological information in a robust and computable fashion. This is essential in order to draw inferences across the levels of granularity which span medicine and biology, an example of which include the understanding of the roles of tumor markers in the development and progress of carcinoma. Such information integration is also important for the integration of genomics information with the information contained in the electronic patient records in such a way that real time conclusions can be drawn. In this paper, we describe a large multi-granular datasource built by using ontological principles and focusing on the case of colon carcinoma.

Bioinformatics and the Library and Information Science Community

Discusses the emergence of bioinformatics in the last three decades as a result of the convergence of networks and genetics. Though a variety of definitions and descriptions of bioinformatics are available, a cross-disciplinary definition eludes. The paper emphasizes that the core activities of library and information science can be of immense value in advancing the theoretical foundations of this emerging subject, as well as developing the mechanisms and tools for quality biomedical information in a timely manner. The creation, management, and dissemination of biomedical knowledge and information for both specialists and for laymen is a challenge and an opportunity for library and information professionals to broaden their professional horizons and to generate new knowledge in the process.

TERMTrial--terminology-based documentation systems for cooperative clinical trials

Within cooperative groups of multi-center clinical trials a standardized documentation is a prerequisite for communication and sharing of data. Standardizing documentation systems means standardizing the underlying terminology. The management and consistent application of terminology systems is a difficult and fault-prone task, which should be supported by appropriate software tools. Today, documentation systems for clinical trials are often implemented as so-called Remote-Data-Entry-Systems (RDE-systems). Although there are many commercial systems, which support the development of RDE-systems there is none offering a comprehensive terminological support. Therefore, we developed the software system TERMTrial which consists of a component for the definition and management of terminology systems for cooperative groups of clinical trials and two components for the terminology-based automatic generation of trial databases and terminology-based interactive design of electronic case report forms (eCRFs). TERMTrial combines the advantages of remote data entry with a comprehensive terminological control.