A deep learning approach using synthetic images for segmenting and estimating 3D orientation of nanoparticles in EM images

BACKGROUND AND OBJECTIVE

Nanoparticles present properties that can be applied to a wide range of fields such as biomedicine, electronics or optics. The type of properties depends on several characteristics, being some of them related with the particle structure. A proper characterization of nanoparticles is crucial since it could affect their applications. To characterize a particle shape and size, the nanotechnologists employ Electron Microscopy (EM) to obtain images of nanoparticles and perform measures over them. This task could be tedious, repetitive and slow, we present a Deep Learning method based on Convolutional Neural Networks (CNNs) to detect, segment, infer orientations and reconstruct microscope images of nanoparticles. Since machine learning algorithms depend on annotated data and there is a lack of annotated datasets of nanoparticles, our work makes use of artificial datasets of images resembling real nanoparticles photographs.

METHODS

Our work is divided into three tasks. Firstly, a method to create annotated datasets of artificial images resembling Scanning Electron Microscope (SEM). Secondly, two models of convolutional neural networks are trained using the artificial datasets previously generated, the first one is in charge of the detection and segmentation of the nanoparticles while the second one will infer the nanoparticle orientation. Finally, the 3D reconstruction module will recreate in a 3D scene the set of detected particles.

RESULTS

We have tested our method with five different shapes of basic nanoparticles: spheres, cubes, ellipsoids, hexagonal discs and octahedrons. An analysis of the reconstructions was conducted by manually comparing each of them with the real images. The results obtained have been promising, the particles are segmented and reconstructed accordingly to their shapes and orientations.

CONCLUSIONS

We have developed a method for nanoparticle detection and segmentation in microscope images. Moreover, we can also infer an approximation of the 3D orientation of the particles and, in conjunction with the detections, create a 3D reconstruction of the photographs. The novelty of our approximation lies in the dataset used. Instead of using annotated images, we have created the datasets simulating the microscope images by using basic geometrical objects that imitate real nanoparticles.

Biomedical Informatics and the Convergence of Nano-Bio-Info-Cogno (NBIC) Technologies

Objectives To analyze the role that biomedical informatics could play in the application of the NBIC Converging Technologies in the medical field and raise awareness of these new areas throughout the Biomedical Informatics community.

Methods Review of the literature and analysis of the reference documents in this domain from the biomedical informatics perspective. Detailing existing developments showing that partial convergence of technologies have already yielded relevant results in biomedicine (such as bioinformatics or biochips). Input from current projects in which the authors are involved is also used.

Results Information processing is a key issue in enabling the convergence of NBIC technologies. Researchers in biomedical informatics are in a privileged position to participate and actively develop this new scientific direction. The experience of biomedical informaticians in five decades of research in the medical area and their involvement in the completion of the Human and other genome projects will help them participate in a similar role for the development of applications of converging technologies —particularly in nanomedicine.

Conclusions The proposed convergence will bring bridges between traditional disciplines. Particular attention should be placed on the ethical, legal, and social issues raised by the NBIC convergence. These technologies provide new directions for research and education in Biomedical Informatics placing a greater emphasis in multidisciplinary approaches.

Section 7: Bioinformatics: Bioinformatics Linkage of Heterogeneous Clinical and Genomic Information in Support of Personalized Medicine

Biomedical Informatics as a whole faces a difficult epistemological task, since there is no foundation to explain the complexities of modeling clinical medicine and the many relationships between genotype, phenotype, and environment. This paper discusses current efforts to investigate such relationships, intended to lead to better diagnostic and therapeutic procedures and the development of treatments that could make personalized medicine a reality.

To achieve this goal there are a number of issues to overcome. Primary are the rapidly growing numbers of heterogeneous data sources which must be integrated to support personalized medicine. Solutions involving the use of domain driven information models of heterogeneous data sources are described in conjunction with controlled ontologies and terminologies. A number of such applications are discussed.

Researchers have realized that many dimensions of biology and medicine aim to understand and model the informational mechanisms that support more precise clinical diagnostic, prognostic and therapeutic procedures. As long as data grows exponentially, novel Biomedical Informatics approaches and tools are needed to manage the data. Although researchers are typically able to manage this information within specific, usually narrow contexts of clinical investigation, novel approaches for both training and clinical usage must be developed.

After some preliminary overoptimistic expectations, it seems clear now that genetics alone cannot transform medicine. In order to achieve this, heterogeneous clinical and genomic data source must be integrated in scientifically meaningful and productive systems. This will include hypothesis-driven scientific research systems along with well understood information systems to support such research. These in turn will enable the faster advancement of personalized medicine.

Establishing an Agenda for Biomedical Informatics

Objective: To describe potential areas of collaboration between Medical Informatics (BI) and Bioinformatics (BI) and their effects on planning future work in both disciplines.

Methods: Some reflections on the objectives and rationale underpinning MI and BI are given, and preliminary results from the BIOINFOMED workgroup, supported by the European Commission, are introduced. Results: Applications from both subfields suggest topics for sharing and exchange between the subfields within the emerging field of Biomedical Informatics. Conclusions: We suggest how the nature and degree of collaboration between the sub-disciplines can impact future work in molecular medicine.

Integrating Genomics into Health Information Systems

Objective: To outline the main issues related to the impact of the data generated by the Human Genome Project on health information systems. A major challenge for medical informatics is identified, consisting of adapting traditional systems to new genetic-based diagnostic and therapeutic tools. Methods: Reviewing and analysing the different health information levels from an organisational complexity point of view. A model is proposed to explain the interactions between health informatics, bioinformatics and molecular medicine.

Results: We suggest a new framework that integrates genetic data into health information systems. Using this model, new topics for future research and development are identified.

Conclusions: We are witnessing the birth of a new era (post-genomics). In this era technological advancements in genomics offer new opportunities for clinical applications. Medical informaticians should play an important role in this new endeavour.

Theory, Abstraction and Design in Medical Informatics

Objective: To analyze the scientific and engineering components of Medical Informatics. A clear characterization of these components should be undertaken to categorize different areas of Medical Informatics and create a research agenda for the future. Methods: We have adapted a classical ACM and IEEE report on computing to analyze Medical Informatics from three different viewpoints: Theory, Abstraction, and Design.

Results: We suggest that Medical Informatics can be considered from these three perspectives: (1) Theory, from which medical informaticians formally characterize the properties of the objects of study, creating new theories or using and adapting existing theories (e.g., from mathematics), (2) Abstraction, from which medical informaticians deal with all aspects of medical information and create new abstractions, methods, and technology-independent models, which can be experimentally verified, and (3) Design, from which medical informaticians develop systems or act as information brokers or advisors between medical and technology professionals, to improve the quality of computer applications in medicine.

Conclusion: Based on this framework, we suggest that Medical Informatics has an independent scientific character, different from other applied informatics areas. Finally, we analyze these three perspectives using data mining in medicine.

Medical Informatics and Bioinformatics: Integration or Evolution through Scientific Crises?

Objectives: To contribute a new perspective on recent investigations into the scientific foundations of medical informatics (MI) and bioinformatics (BI). To support efforts that could generate synergies and new research directions.

Methods: MI and BI are compared and contrasted from a philosophy of science perspective. Historical examples from MI and BI are analyzed based on contrasting viewpoints about the evolution of scientific disciplines.

Results: Our analysis suggests that the scientific approaches of MI and BI involve different assumptions and foundations, which, together with largely non-overlapping communities of researchers for the two disciplines, have led to different courses of development. We indicate how their respective application domains, medicine, and biology may have contributed to these differences in development.

Conclusions: An analysis from the point of view of the philosophy of science is characteristic of established scientific disciplines. From a Kuhnian perspective, both disciplines may be entering a period of scientific crisis, where their foundations are questioned and where new ideas (or paradigm shifts) and a progressive research programme are needed to advance them scientifically. We discuss research directions and trends both supporting and challenging integration of the subdisciplines of MI and BI into a unified field of biomedical informatics (BMI), centered around the evolution of information cybernetics.

Designing New Methodologies for Integrating Biomedical Information in Clinical Trials

Objectives: To propose a modification to current methodologies for clinical trials, improving data collection and cost-efficiency. To describe a system to integrate distributed and heterogeneous medical and genetic databases for improving information access, retrieval and analysis of biomedical information.

Methods: Data for clinical trials can be collected from remote, distributed and heterogeneous data sources.

In this distributed scenario, we propose an ontology-based approach, with two basic operations: mapping and unification. Mapping outputs the semantic model of a virtual repository with the information model of a specific database. Unification provides a single schema for two or more previously available virtual repositories. In both processes, domain ontologies can improve other traditional approaches.

Results: Private clinical databases and public genomic and disease databases (e.g., OMIM, Prosite and others) were integrated. We successfully tested the system using thirteen databases containing clinical and biological information and biomedical vocabularies.

Conclusions: We present a domain-independent approach to biomedical database integration, used in this paper as a reference for the design of future models of clinico-genomic trials where information will be integrated, retrieved and analyzed. Such an approach to biomedical data integration has been one of the goals of the IST INFOBIOMED Network of Excellence in Biomedical Informatics, funded by the European Commission, and the new ACGT (Advanced Clinico-Genomic Trials on Cancer) project, where the authors will apply these methods to research experiments.

Grid Requirements for the Integration of Biomedical Information Resources for Health Applications

Objectives: The goal of this paper is to identify how Grid technology can be applied for the development and deployment of integration systems, bringing together distributed and heterogeneous biomedical information sources for medical applications.

Methods: The integration of new genetic and medical knowledge in clinical workflows requires the development of new paradigms for information management in which the ability to access and relate disparate data sources is essential. We adopt a requirements perspective based on the user needs we have identified in the development of the INFOGENMED system to assess current Grid technology against those requirements.

Results: The gap between Grid features and distributed biomedical information integration needs is characterized. Results from prospective studies are also reported.

Conclusions: Grid infrastructures offer advanced features for the deployment of collaborative computational environments across virtual organizations. New Grid developments are in line with the problem of multiple site information integration. From the INFOGENMED point of view, Grid infrastructures need to evolve to implement structured data access services and semantic content description and discovery.

Bioinformatics: Towards New Directions for Public Health

Objectives: Epidemiologists are reformulating their classical approaches to diseases by considering various issues associated to “omics” areas and technologies. Traditional differences between epidemiology and genetics include background, training, terminologies, study designs and others. Public health and epidemiology are increasingly looking forward to using methodologies and informatics tools, facilitated by the Bioinformatics community, for managing genomic information. Our aim is to describe which are the most important implications related with the increasing use of genomic information for public health practice, research and education. To review the contribution of bioinformatics to these issues, in terms of providing the methods and tools needed for processing genetic information from pathogens and patients. To analyze the research challenges in biomedical informatics related with the need of integration of clinical, environmental and genetic data and the new scenarios arisen in public health.

Methods: Review of the literature, Internet resources and material and reports generated by internal and external research projects.

Results: New developments are needed to advance in the study of the interactions between environmental agents and genetic factors involved in the development of diseases. The use of biomarkers, biobanks, and integrated genomic/clinical databases poses serious challenges for informaticians in order to extract useful information and knowledge for public health, biomedical research and healthcare.

Conclusions: From an informatics perspective, integrated medical/biological ontologies and new semantic-based models for managing information provide new challenges for research in areas such as genetic epidemiology and the “omics” disciplines, among others. In this regard, there are various ethical, privacy, informed consent and social implications, that should be carefully addressed by researchers, practitioners and policy makers.

Using Web Services for Linking Genomic Data to Medical Information Systems

Objectives: To develop a new perspective for biomedical information systems, regarding the introduction of ideas, methods and tools related to the new scenario of genomic medicine.

Methods: Technological aspects related to the analysis and integration of heterogeneous clinical and genomic data include mapping clinical and genetic concepts, potential future standards or the development of integrated biomedical ontologies. In this clinicomics scenario, we describe the use of Web services technologies to improve access to and integrate different information sources. We give a concrete example of the use of Web services technologies: the Onto Fusion project.

Results: Web services provide new biomedical informatics (BMI) approaches related to genomic medicine. Customized workflowswill aid research tasks by linking heterogeneous Web services. Two significant examples of these European Commission-funded efforts are the INFOBIOMED Network of Excellence and the Advancing Clinico-Genomic Trials on Cancer (ACGT) integrated project.

Conclusions: Supplying medical researchers and practitioners with omicsdata and biologists with clinical datasets can help to develop genomic medicine. BMI is contributing by providing the informatics methods and technological infrastructure needed for these collaborative efforts.

Discussion of “Biomedical Infor -matics: We Are What We Publish”

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Biomedical Informatics: We Are What We Publish", written by Peter L. Elkin, Steven H. Brown, and Graham Wright. It is introduced by an editorial. This article contains the combined commenta -ries invited to independently comment on the Elkin et al. paper. In subsequent issues the discussion can continue through letters to the editor.

Carbon Nanotubes' Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra

This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (J_m) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of J_m showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of J_m for other CNTs was provided by random forest using eight features, obtaining test R-squared (R²) of 0.863 and test root-mean-square error (RMSE) of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG) transform with a potential applicability as predictive tools in nanotechnology and material risk assessments.

A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov

BACKGROUND

Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results.

METHODS

We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset.

RESULTS AND CONCLUSIONS

The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.

Toward a view-oriented approach for aligning RDF-based biomedical repositories

INTRODUCTION

This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems".

BACKGROUND

The need for complementary access to multiple RDF databases has fostered new lines of research, but also entailed new challenges due to data representation disparities. While several approaches for RDF-based database integration have been proposed, those focused on schema alignment have become the most widely adopted. All state-of-the-art solutions for aligning RDF-based sources resort to a simple technique inherited from legacy relational database integration methods. This technique - known as element-to-element (e2e) mappings - is based on establishing 1:1 mappings between single primitive elements - e.g. concepts, attributes, relationships, etc. - belonging to the source and target schemas. However, due to the intrinsic nature of RDF - a representation language based on defining tuples < subject, predicate, object > -, one may find RDF elements whose semantics vary dramatically when combined into a view involving other RDF elements - i.e. they depend on their context. The latter cannot be adequately represented in the target schema by resorting to the traditional e2e approach. These approaches fail to properly address this issue without explicitly modifying the target ontology, thus lacking the required expressiveness for properly reflecting the intended semantics in the alignment information.

OBJECTIVES

To enhance existing RDF schema alignment techniques by providing a mechanism to properly represent elements with context-dependent semantics, thus enabling users to perform more expressive alignments, including scenarios that cannot be adequately addressed by the existing approaches.

METHODS

Instead of establishing 1:1 correspondences between single primitive elements of the schemas, we propose adopting a view-based approach. The latter is targeted at establishing mapping relationships between RDF subgraphs - that can be regarded as the equivalent of views in traditional databases -, rather than between single schema elements. This approach enables users to represent scenarios defined by context-dependent RDF elements that cannot be properly represented when adopting the currently existing approaches.

RESULTS

We developed a software tool implementing our view-based strategy. Our tool is currently being used in the context of the European Commission funded p-medicine project, targeted at creating a technological framework to integrate clinical and genomic data to facilitate the development of personalized drugs and therapies for cancer, based on the genetic profile of the patient. We used our tool to integrate different RDF-based databases - including different repositories of clinical trials and DICOM images - using the Health Data Ontology Trunk (HDOT) ontology as the target schema.

CONCLUSIONS

The importance of database integration methods and tools in the context of biomedical research has been widely recognized. Modern research in this area - e.g. identification of disease biomarkers, or design of personalized therapies - heavily relies on the availability of a technical framework to enable researchers to uniformly access disparate repositories. We present a method and a tool that implement a novel alignment method specifically designed to support and enhance the integration of RDF-based data sources at schema (metadata) level. This approach provides an increased level of expressiveness compared to other existing solutions, and allows solving heterogeneity scenarios that cannot be properly represented using other state-of-the-art techniques.

Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research

Nanotechnology represents an area of particular promise and significant opportunity across multiple scientific disciplines. Ongoing nanotechnology research ranges from the characterization of nanoparticles and nanomaterials to the analysis and processing of experimental data seeking correlations between nanoparticles and their functionalities and side effects. Due to their special properties, nanoparticles are suitable for cellular-level diagnostics and therapy, offering numerous applications in medicine, e.g. development of biomedical devices, tissue repair, drug delivery systems and biosensors. In nanomedicine, recent studies are producing large amounts of structural and property data, highlighting the role for computational approaches in information management. While in vitro and in vivo assays are expensive, the cost of computing is falling. Furthermore, improvements in the accuracy of computational methods (e.g. data mining, knowledge discovery, modeling and simulation) have enabled effective tools to automate the extraction, management and storage of these vast data volumes. Since this information is widely distributed, one major issue is how to locate and access data where it resides (which also poses data-sharing limitations). The novel discipline of nanoinformatics addresses the information challenges related to nanotechnology research. In this paper, we summarize the needs and challenges in the field and present an overview of extant initiatives and efforts.

Discussion of "Biomedical informatics: we are what we publish"

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Biomedical Informatics: We Are What We Publish", written by Peter L. Elkin, Steven H. Brown, and Graham Wright. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the Elkin et al. paper. In subsequent issues the discussion can continue through letters to the editor.

Using nanoinformatics methods for automatically identifying relevant nanotoxicology entities from the literature

Nanoinformatics is an emerging research field that uses informatics techniques to collect, process, store, and retrieve data, information, and knowledge on nanoparticles, nanomaterials, and nanodevices and their potential applications in health care. In this paper, we have focused on the solutions that nanoinformatics can provide to facilitate nanotoxicology research. For this, we have taken a computational approach to automatically recognize and extract nanotoxicology-related entities from the scientific literature. The desired entities belong to four different categories: nanoparticles, routes of exposure, toxic effects, and targets. The entity recognizer was trained using a corpus that we specifically created for this purpose and was validated by two nanomedicine/nanotoxicology experts. We evaluated the performance of our entity recognizer using 10-fold cross-validation. The precisions range from 87.6% (targets) to 93.0% (routes of exposure), while recall values range from 82.6% (routes of exposure) to 87.4% (toxic effects). These results prove the feasibility of using computational approaches to reliably perform different named entity recognition (NER)-dependent tasks, such as for instance augmented reading or semantic searches. This research is a "proof of concept" that can be expanded to stimulate further developments that could assist researchers in managing data, information, and knowledge at the nanolevel, thus accelerating research in nanomedicine.

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.

Biomedical informatics publications: a global perspective. Part II: Journals

BACKGROUND

Biomedical Informatics (BMI) is a broad discipline, having evolved from both Medical Informatics (MI) and Bioinformatics (BI). An analysis of publications in the fieldshould provide an indication about the geographic distribution of BMI research contributions and possible lessons for the future, both for research and professional practice.

OBJECTIVES

In part I of our analysis of biomedical informatics publications we presented results from BMI conferences. In this second part, we analyse BMI journals, which provide a broader perspective and comparison between data from conferences and journals that ought to confirm or suggest alternatives to the original distributional findings from the conferences.

METHODS

We manually collected data about authors and their geographical origin from various MI journals: the International Journal of Medical Informatics (IJMI), the Journal of Biomedical Informatics (JBI), Methods of In formation in Medicine (MIM) and The Journal of the American Medical Informatics Association (JAMIA). Focusing on first authors, we also compared these findings with data from the journal Bioinformatics.

RESULTS

Our results confirm those obtained in our analysis of BMI conferences - that local and regional authors favor their corresponding MI journals just as they do their conferences. Consideration of other factors, such as the increasingly open source nature of data and software tools, is consistent with these findings.

CONCLUSIONS

Our analysis suggests various indicators that could lead to further, deeper analyses, and could provide additional insights for future BMI research and professional activities.

Biomedical informatics publications: a global perspective: part I: conferences

BACKGROUND

In the past decade, Medical Informatics (MI) and Bioinformatics (BI) have converged towards a new discipline, called Biomedical Informatics (BMI) bridging informatics methods across the spectrum from genomic research to personalized medicine and global healthcare. This convergence still raises challenging research questions which are being addressed by researchers internationally, which in turn raises the question of how biomedical informatics publications reflect the contributions from around the world in documenting the research.

OBJECTIVES

To analyse the worldwide participation of biomedical informatics researchers from professional groups and societies in the best-known scientific conferences in the field. The analysis is focused on their geographical affiliation, but also includes other features, such as the impact and recognition of the conferences.

METHODS

We manually collected data about authors of papers presented at three major MI conferences: Medinfo, MIE and the AMIA symposium. In addition, we collected data from a BI conference, ISMB, as a comparison. Finally, we analyzed the impact and recognition of these conferences within their scientific contexts.

RESULTS

Data indicate a predominance of local authors at the regional conferences (AMIA and MIE), whereas other conferences with a world-wide scope (Medinfo and ISMB) had broader participation. Our analysis shows that the influence of these conferences beyond the discipline remains somewhat limited.

CONCLUSIONS

Our results suggest that for BMI to be recognized as a broad discipline, both in the geographical and scientific sense, it will need to extend the scope of collaborations and their interdisciplinary impacts worldwide.

Biomedical ontologies: toward scientific debate

OBJECTIVES

Biomedical ontologies have been very successful in structuring knowledge for many different applications, receiving widespread praise for their utility and potential. Yet, the role of computational ontologies in scientific research, as opposed to knowledge management applications, has not been extensively discussed. We aim to stimulate further discussion on the advantages and challenges presented by biomedical ontologies from a scientific perspective.

METHODS

We review various aspects of biomedical ontologies going beyond their practical successes, and focus on some key scientific questions in two ways. First, we analyze and discuss current approaches to improve biomedical ontologies that are based largely on classical, Aristotelian ontological models of reality. Second, we raise various open questions about biomedical ontologies that require further research, analyzing in more detail those related to visual reasoning and spatial ontologies.

RESULTS

We outline significant scientific issues that biomedical ontologies should consider, beyond current efforts of building practical consensus between them. For spatial ontologies, we suggest an approach for building "morphospatial" taxonomies, as an example that could stimulate research on fundamental open issues for biomedical ontologies.

CONCLUSIONS

Analysis of a large number of problems with biomedical ontologies suggests that the field is very much open to alternative interpretations of current work, and in need of scientific debate and discussion that can lead to new ideas and research directions.

International efforts in nanoinformatics research applied to nanomedicine

BACKGROUND

Nanomedicine and nanoinformatics are novel disciplines facing substantial challenges. Since nanomedicine involves complex and massive data analysis and management, a new discipline named nanoinformatics is now emerging to provide the vision and the informatics methods and tools needed for such purposes. Methods from biomedi-cal informatics may prove applicable with some adaptation despite nanomedicine involving different biophysical and biochemical characteristics of nanomaterials and corresponding differences in information complexity.

OBJECTIVES

We analyze recent initiatives and opportunities for research in nanomedicine and nanoinformatics as well as the previous experience of the authors, particularly in the context of a European project named ACTION-Grid. In this project the authors aimed to create a collaborative environment in biomedical and nanomedical research among countries in Europe, Western Balkans, Latin America, North Africa and the USA.

METHODS

We review and analyze the rationale and scientific issues behind the new fields of nanomedicine and nanoinformatics. Such a review is linked to actual research projects and achievements of the authors within their groups.

RESULTS

The work of the authors at the intersection between these two areas is presented. We also analyze several research initiatives that have recently emerged in the EU and USA context and highlight some ideas for future action at the international level.

CONCLUSIONS

Nanoinformatics aims to build new bridges between medicine, nanotechnology and informatics, allowing the application of computational methods in the nano-related areas. Opportunities for world-wide collaboration are already emerging and will be influential in advancing the field.

PubDNA Finder: a web database linking full-text articles to sequences of nucleic acids

SUMMARY

PubDNA Finder is an online repository that we have created to link PubMed Central manuscripts to the sequences of nucleic acids appearing in them. It extends the search capabilities provided by PubMed Central by enabling researchers to perform advanced searches involving sequences of nucleic acids. This includes, among other features (i) searching for papers mentioning one or more specific sequences of nucleic acids and (ii) retrieving the genetic sequences appearing in different articles. These additional query capabilities are provided by a searchable index that we created by using the full text of the 176 672 papers available at PubMed Central at the time of writing and the sequences of nucleic acids appearing in them. To automatically extract the genetic sequences occurring in each paper, we used an original method we have developed. The database is updated monthly by automatically connecting to the PubMed Central FTP site to retrieve and index new manuscripts. Users can query the database via the web interface provided.

AVAILABILITY

PubDNA Finder can be freely accessed at http://servet.dia.fi.upm.es:8080/pubdnafinder

A method for automatically extracting infectious disease-related primers and probes from the literature

Background

Primer and probe sequences are the main components of nucleic acid-based detection systems. Biologists use primers and probes for different tasks, some related to the diagnosis and prescription of infectious diseases. The biological literature is the main information source for empirically validated primer and probe sequences. Therefore, it is becoming increasingly important for researchers to navigate this important information. In this paper, we present a four-phase method for extracting and annotating primer/probe sequences from the literature. These phases are: (1) convert each document into a tree of paper sections, (2) detect the candidate sequences using a set of finite state machine-based recognizers, (3) refine problem sequences using a rule-based expert system, and (4) annotate the extracted sequences with their related organism/gene information.

Results

We tested our approach using a test set composed of 297 manuscripts. The extracted sequences and their organism/gene annotations were manually evaluated by a panel of molecular biologists. The results of the evaluation show that our approach is suitable for automatically extracting DNA sequences, achieving precision/recall rates of 97.98% and 95.77%, respectively. In addition, 76.66% of the detected sequences were correctly annotated with their organism name. The system also provided correct gene-related information for 46.18% of the sequences assigned a correct organism name.

Conclusions

We believe that the proposed method can facilitate routine tasks for biomedical researchers using molecular methods to diagnose and prescribe different infectious diseases. In addition, the proposed method can be expanded to detect and extract other biological sequences from the literature. The extracted information can also be used to readily update available primer/probe databases or to create new databases from scratch.

Integration of relational and textual biomedical sources. A pilot experiment using a semi-automated method for logical schema acquisition

OBJECTIVES

Bringing together structured and text-based sources is an exciting challenge for biomedical informaticians, since most relevant biomedical sources belong to one of these categories. In this paper we evaluate the feasibility of integrating relational and text-based biomedical sources using: i) an original logical schema acquisition method for textual databases developed by the authors, and ii) OntoFusion, a system originally designed by the authors for the integration of relational sources.

METHODS

We conducted an integration experiment involving a test set of seven differently structured sources covering the domain of genetic diseases. We used our logical schema acquisition method to generate schemas for all textual sources. The sources were integrated using the methods and tools provided by OntoFusion. The integration was validated using a test set of 500 queries.

RESULTS

A panel of experts answered a questionnaire to evaluate i) the quality of the extracted schemas, ii) the query processing performance of the integrated set of sources, and iii) the relevance of the retrieved results. The results of the survey show that our method extracts coherent and representative logical schemas. Experts' feedback on the performance of the integrated system and the relevance of the retrieved results was also positive. Regarding the validation of the integration, the system successfully provided correct results for all queries in the test set.

CONCLUSIONS

The results of the experiment suggest that text-based sources including a logical schema can be regarded as equivalent to structured databases. Using our method, previous research and existing tools designed for the integration of structured databases can be reused - possibly subject to minor modifications - to integrate differently structured sources.

Biomedical informatics and the convergence of Nano-Bio-Info-Cogno (NBIC) technologies

OBJECTIVES

To analyze the role that biomedical informatics could play in the application of the NBIC Converging Technologies in the medical field and raise awareness of these new areas throughout the Biomedical Informatics community.

METHODS

Review of the literature and analysis of the reference documents in this domain from the biomedical informatics perspective. Detailing existing developments showing that partial convergence of technologies have already yielded relevant results in biomedicine (such as bioinformatics or biochips). Input from current projects in which the authors are involved is also used.

RESULTS

Information processing is a key issue in enabling the convergence of NBIC technologies. Researchers in biomedical informatics are in a privileged position to participate and actively develop this new scientific direction. The experience of biomedical informaticians in five decades of research in the medical area and their involvement in the completion of the Human and other genome projects will help them participate in a similar role for the development of applications of converging technologies -particularly in nanomedicine.

CONCLUSIONS

The proposed convergence will bring bridges between traditional disciplines. Particular attention should be placed on the ethical, legal, and social issues raised by the NBIC convergence. These technologies provide new directions for research and education in Biomedical Informatics placing a greater emphasis in multidisciplinary approaches.

Bioinformatics linkage of heterogeneous clinical and genomic information in support of personalized medicine

OBJECTIVES

Biomedical Informatics as a whole faces a difficult epistemological task, since there is no foundation to explain the complexities of modeling clinical medicine and the many relationships between genotype, phenotype, and environment. This paper discusses current efforts to investigate such relationships, intended to lead to better diagnostic and therapeutic procedures and the development of treatments that could make personalized medicine a reality.

METHODS

To achieve this goal there are a number of issues to overcome. Primary are the rapidly growing numbers of heterogeneous data sources which must be integrated to support personalized medicine. Solutions involving the use of domain driven information models of heterogeneous data sources are described in conjunction with controlled ontologies and terminologies. A number of such applications are discussed.

RESULTS

Researchers have realized that many dimensions of biology and medicine aim to understand and model the informational mechanisms that support more precise clinical diagnostic, prognostic and therapeutic procedures. As long as data grows exponentially, novel Biomedical Informatics approaches and tools are needed to manage the data. Although researchers are typically able to manage this information within specific, usually narrow contexts of clinical investigation, novel approaches for both training and clinical usage must be developed.

CONCLUSION

After some preliminary overoptimistic expectations, it seems clear now that genetics alone cannot transform medicine. In order to achieve this, heterogeneous clinical and genomic data source must be integrated in scientifically meaningful and productive systems. This will include hypothesis-driven scientific research systems along with well understood information systems to support such research. These in turn will enable the faster advancement of personalized medicine.

An agent- and ontology-based system for integrating public gene, protein, and disease databases

In this paper, we describe OntoFusion, a database integration system. This system has been designed to provide unified access to multiple, heterogeneous biological and medical data sources that are publicly available over Internet. Many of these databases do not offer a direct connection, and inquiries must be made via Web forms, returning results as HTML pages. A special module in the OntoFusion system is needed to integrate these public 'Web-based' databases. Domain ontologies are used to do this and provide database mapping and unification. We have used the system to integrate seven significant and widely used public biomedical databases: OMIM, PubMed, Enzyme, Prosite and Prosite documentation, PDB, SNP, and InterPro. A case study is detailed in depth, showing system performance. We analyze the system's architecture and methods and discuss its use as a tool for biomedical researchers.

Medical informatics and bioinformatics: integration or evolution through scientific crises?

OBJECTIVES

To contribute a new perspective on recent investigations into the scientific foundations of medical informatics (MI) and bioinformatics (BI). To support efforts that could generate synergies and new research directions.

METHODS

MI and BI are compared and contrasted from a philosophy of science perspective. Historical examples from MI and BI are analyzed based on contrasting viewpoints about the evolution of scientific disciplines.

RESULTS

Our analysis suggests that the scientific approaches of MI and BI involve different assumptions and foundations, which, together with largely non-overlapping communities of researchers for the two disciplines, have led to different courses of development. We indicate how their respective application domains, medicine, and biology may have contributed to these differences in development.

CONCLUSIONS

An analysis from the point of view of the philosophy of science is characteristic of established scientific disciplines. From a Kuhnian perspective, both disciplines may be entering a period of scientific crisis, where their foundations are questioned and where new ideas (or paradigm shifts) and a progressive research programme are needed to advance them scientifically. We discuss research directions and trends both supporting and challenging integration of the subdisciplines of MI and BI into a unified field of biomedical informatics (BMI), centered around the evolution of information cybernetics.

Designing new methodologies for integrating biomedical information in clinical trials

OBJECTIVES

To propose a modification to current methodologies for clinical trials, improving data collection and cost-efficiency. To describe a system to integrate distributed and heterogeneous medical and genetic databases for improving information access, retrieval and analysis of biomedical information.

METHODS

Data for clinical trials can be collected from remote, distributed and heterogeneous data sources. In this distributed scenario, we propose an ontologybased approach, with two basic operations: mapping and unification. Mapping outputs the semantic model of a virtual repository with the information model of a specific database. Unification provides a single schema for two or more previously available virtual repositories. In both processes, domain ontologies can improve other traditional approaches.

RESULTS

Private clinical databases and public genomic and disease databases (e.g., OMIM, Prosite and others) were integrated. We successfully tested the system using thirteen databases containing clinical and biological information and biomedical vocabularies.

CONCLUSIONS

We present a domain-independent approach to biomedical database integration, used in this paper as a reference for the design of future models of clinico-genomic trials where information will be integrated, retrieved and analyzed. Such an approach to biomedical data integration has been one of the goals of the IST INFOBIOMED Network of Excellence in Biomedical Informatics, funded by the European Commission, and the new ACGT (Advanced Clinico-Genomic Trials on Cancer) project, where the authors will apply these methods to research experiments.

ONTOFUSION: ontology-based integration of genomic and clinical databases

ONTOFUSION is an ontology-based system designed for biomedical database integration. It is based on two processes: mapping and unification. Mapping is a semi-automated process that uses ontologies to link a database schema with a conceptual framework-named virtual schema. There are three methodologies for creating virtual schemas, according to the origin of the domain ontology used: (1) top-down--e.g. using an existing ontology, such as the UMLS or Gene Ontology--, (2) bottom-up--building a new domain ontology-- and (3) a hybrid combination. Unification is an automated process for integrating ontologies and hence the database to which they are linked. Using these methods, we employed ONTOFUSION to integrate a large number of public genomic and clinical databases, as well as biomedical ontologies.

Grid requirements for the integration of biomedical information resources for health applications

OBJECTIVES

The goal of this paper is to identify how Grid technology can be applied for the development and deployment of integration systems, bringing together distributed and heterogeneous biomedical information sources for medical applications.

METHODS

The integration of new genetic and medical knowledge in clinical workflows requires the development of new paradigms for information management in which the ability to access and relate disparate data sources is essential. We adopt a requirements perspective based on the user needs we have identified in the development of the INFOGENMED system to assess current Grid technology against those requirements.

RESULTS

The gap between Grid features and distributed biomedical information integration needs is characterized. Results from prospective studies are also reported.

CONCLUSIONS

Grid infrastructures offer advanced features for the deployment of collaborative computational environments across virtual organizations. New Grid developments are in line with the problem of multiple site information integration. From the INFOGENMED point of view, Grid infrastructures need to evolve to implement structured data access services and semantic content description and discovery.

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

In this paper, we review the results of BIOINFOMED, a study funded by the European Commission (EC) with the purpose to analyse the different issues and challenges in the area where Medical Informatics and Bioinformatics meet. Traditionally, Medical Informatics has been focused on the intersection between computer science and clinical medicine, whereas Bioinformatics have been predominantly centered on the intersection between computer science and biological research. Although researchers from both areas have occasionally collaborated, their training, objectives and interests have been quite different. The results of the Human Genome and related projects have attracted the interest of many professionals, and introduced new challenges that will transform biomedical research and health care. A characteristic of the 'post genomic' era will be to correlate essential genotypic information with expressed phenotypic information. In this context, Biomedical Informatics (BMI) has emerged to describe the technology that brings both disciplines (BI and MI) together to support genomic medicine. In recognition of the dynamic nature of BMI, institutions such as the EC have launched several initiatives in support of a research agenda, including the BIOINFOMED study.

Public Health Implications of Bioinformatics

Epidemiologists are reformulating their classical approaches to diseases by considering various issues associated to “omics” areas and technologies. Traditional differences between epidemiology and genetics include background, training, terminologies, study designs and others. Public health and epidemiology are increasingly looking forward to using methodologies and informatics tools, facilitated by the Bioinformatics community, for managing genomic information. Future microarray developments will also facilitate the analysis of entire genomes on single arrays, enhancing genetic epidemiology research. The use of biomarkers, biobanks, and integrated genomic/clinical databases poses serious challenges for bioinformaticians in order to extract useful information and knowledge for biomedical research and healthcare. In this regard, there are various ethical, privacy, informed consent and social implications that should be carefully addressed by researchers, practitioners and policy makers.

Bioinformatics: towards new directions for public health

OBJECTIVES

Epidemiologists are reformulating their classical approaches to diseases by considering various issues associated to "omics" areas and technologies. Traditional differences between epidemiology and genetics include background, training, terminologies, study designs and others. Public health and epidemiology are increasingly looking forward to using methodologies and informatics tools, facilitated by the Bioinformatics community, for managing genomic information. Our aim is to describe which are the most important implications related with the increasing use of genomic information for public health practice, research and education. To review the contribution of bioinformatics to these issues, in terms of providing the methods and tools needed for processing genetic information from pathogens and patients. To analyze the research challenges in biomedical informatics related with the need of integration of clinical, environmental and genetic data and the new scenarios arisen in public health.

METHODS

Review of the literature, Internet resources and material and reports generated by internal and external research projects.

RESULTS

New developments are needed to advance in the study of the interactions between environmental agents and genetic factors involved in the development of diseases. The use of biomarkers, biobanks, and integrated genomic/clinical databases poses serious challenges for informaticians in order to extract useful information and knowledge for public health, biomedical research and healthcare.

CONCLUSIONS

From an informatics perspective, integrated medical/biological ontologies and new semantic-based models for managing information provide new challenges for research in areas such as genetic epidemiology and the "omics" disciplines, among others. In this regard, there are various ethical, privacy, informed consent and social implications, that should be carefully addressed by researchers, practitioners and policy makers.

Establishing an agenda for biomedical informatics

OBJECTIVE

To describe potential areas of collaboration between Medical Informatics (BI) and Bioinformatics (BI) and their effects on planning future work in both disciplines.

METHODS

Some reflections on the objectives and rationale underpinning MI and BI are given, and preliminary results from the BIOINFOMED workgroup, supported by the European Commission, are introduced.

RESULTS

Applications from both subfields suggest topics for sharing and exchange between the subfields within the emerging field of Biomedical Informatics.

CONCLUSIONS

We suggest how the nature and degree of collaboration between the sub-disciplines can impact future work in molecular medicine.

Theory, abstraction and design in medical informatics

OBJECTIVE

To analyze the scientific and engineering components of Medical Informatics. A clear characterization of these components should be undertaken to categorize different areas of Medical Informatics and create a research agenda for the future.

METHODS

We have adapted a classical ACM and IEEE report on computing to analyze Medical Informatics from three different viewpoints: Theory, Abstraction, and Design.

RESULTS

We suggest that Medical Informatics can be considered from these three perspectives: (1) Theory, from which medical informaticians formally characterize the properties of the objects of study, creating new theories or using and adapting existing theories (e.g., from mathematics), (2) Abstraction, from which medical informaticians deal with all aspects of medical information and create new abstractions, methods, and technology-independent models, which can be experimentally verified, and (3) Design, from which medical informaticians develop systems or act as information brokers or advisors between medical and technology professionals, to improve the quality of computer applications in medicine.

CONCLUSION

Based on this framework, we suggest that Medical Informatics has an independent scientific character, different from other applied informatics areas. Finally, we analyze these three perspectives using data mining in medicine.

Integrating genomics into health information systems

OBJECTIVE

To outline the main issues related to the impact of the data generated by the Human Genome Project on health information systems. A major challenge for medical informatics is identified, consisting of adapting traditional systems to new genetic-based diagnostic and therapeutic tools.

METHODS

Reviewing and analysing the different health information levels from an organisational complexity point of view. A model is proposed to explain the interactions between health informatics, bioinformatics and molecular medicine.

RESULTS

We suggest a new framework that integrates genetic data into health information systems. Using this model, new topics for future research and development are identified.

CONCLUSIONS

We are witnessing the birth of a new era (post-genomics). In this era technological advancements in genomics offer new opportunities for clinical applications. Medical informaticians should play an important role in this new endeavour.

Medical informatics and bioinformatics: European efforts to facilitate synergy

Over the past decade there have been several attempts to rethink the basic strategies and scope of medical informatics. Meanwhile, bioinformatics has only recently experienced a similar debate about its scientific character. Both disciplines envision the development of novel diagnostic, therapeutic, and management tools, and products for patient care. A combination of the expertise of medical informatics in developing clinical applications and the focused principles that have guided bioinformatics could create a synergy between the two areas of application. Such interaction could have a great influence on future health research and the ultimate goal, namely continuity and individualization of health care. This article summarizes current activities related to facilitating synergy between medical informatics and bioinformatics, emphasizing activities in Europe while relating them to efforts in other parts of the world. The report provides examples of the analysis that European investigators are carrying out, aiming to propose new ideas for collaborations between medical informatics and bioinformatics researchers in a variety of areas.

A concept model for the automatic maintenance of controlled medical vocabularies

A controlled medical vocabulary is a fundamental requirement in a range of medical informatics applications. Large vocabularies development and maintenance is labor intensive and costly. Maintainers of medical vocabularies need appropriate tools to do their work correctly. In this paper, we describe our concept model for a controlled medical vocabulary. We present how this model can check vocabulary consistency. We propose a set of tools in a distributed environment, which permits edition, visualization and maintenance of medical terminologies.

Disseminating multimedia protocols over Internet for emergency and catastrophe management

Over the last years we have developed various computing methods to assist specialized personnel on various aspects of catastrophe and emergency management. New models can address tasks such as patient triage; stabilization, resource coordination and hospital alertness and techniques based on information technologies. In this paper we present various tools (written on Java and C+2) that we created to store, represent, and disseminate practice guidelines and protocols over the World Wide Web. Guidelines and protocols are stored using a standard database program (e.g., Microsoft Access), and represented in a flowchart format linked to multimedia information such as text, pictures, sound, video or external sources of data. Using our JAVA tool, protocols can be disseminated over the Web and viewed with any browser with JAVA compliance. We have implemented 15 emergency protocols that we developed in collaboration with specialized military personnel from the Ministry of Defense, Spain. Users can access remotely those electronic protocols comparing their procedures and methods. Our goal is to enhance agreement and consensus among remote medical centers regarding emergency and catastrophe management, establishing discussions over the network. Our tools have also a potential for training medical and paramedical personnel for emergency situations.

A JAVA-based multimedia tool for clinical practice guidelines

We have developed a specific language for the representation of Clinical Practice Guidelines (CPGs) and Windows C++ and platform independent JAVA applications for multimedia presentation and edition of electronically stored CPGs. This approach facilitates translation of guidelines and protocols from paper to computer-based flowchart representations. Users can navigate through the algorithm with a friendly user interface and access related multimedia information within the context of each clinical problem. CPGs can be stored in a computer server and distributed over the World Wide Web, facilitating dissemination, local adaptation, and use as a reference element in medical care. We have chosen the Agency for Health Care and Policy Research's heart failure guideline to demonstrate the capabilities of our tool.

ARMEDA: accessing remote medical databases over the World Wide Web

We have created a computer system to access medical information located at remote databases over the World Wide Web, for epidemiological ad health services research. We made a preliminary prototype where a specific database model could be searched and information be retrieved. We are currently working on a new component-based architecture, where different databases scheme from various sites can be used to create a unified model, giving users a "virtual" vision of a single, local database. Different software engineering and artificial intelligence methods are used to access, integrate, filter and deliver information to users.