A method and software framework for enriching private biomedical sources with data from public online repositories.

Modern biomedical research relies on the semantic integration of heterogeneous data sources to find data correlations. Researchers access multiple datasets of disparate origin, and identify elements-e.g. genes, compounds, pathways-that lead to interesting correlations. Normally, they must refer to additional public databases in order to enrich the information about the identified entities-e.g. scientific literature, published clinical trial results, etc. While semantic integration techniques have traditionally focused on providing homogeneous access to private datasets-thus helping automate the first part of the research, and there exist different solutions for browsing public data, there is still a need for tools that facilitate merging public repositories with private datasets. This paper presents a framework that automatically locates public data of interest to the researcher and semantically integrates it with existing private datasets. The framework has been designed as an extension of traditional data integration systems, and has been validated with an existing data integration platform from a European research project by integrating a private biological dataset with data from the National Center for Biotechnology Information (NCBI).

Towards the taxonomic categorization and recognition of nanoparticle shapes.

The shape of nanoparticles and nanomaterials is a fundamental characteristic that has been shown to influence a number of their properties and effects, particularly for nanomedical applications. The information related with this feature of nanoparticles and nanomaterials is, therefore, crucial to exploit and foster in existing and future research in this area. We have found that descriptions of morphological and spatial properties are consistently reported in the nanotechnology literature, and in general, these morphological properties can be observed and measured using various microscopy techniques. In this paper, we outline a taxonomy of nanoparticle shapes constructed according to nanotechnologists' descriptions and formal geometric concepts that can be used to address the problem of nanomaterial categorization. We employ an image segmentation technique, belonging to the mathematical morphology field, which is capable of identifying shapes in images that can be used to (semi-) automatically annotate nanoparticle images. FROM THE CLINICAL EDITOR: This team of authors outlines a taxonomy of nanoparticle shapes constructed according to nanotechnologists' descriptions and formal geometric concepts enabling nanomaterial categorization. They also employ a mathematical morphology-based image segmentation system, capable of identifying shapes and can be utilized in semi-automated annotation of nanoparticle images.

Assessing the prognoses on Health care in the information society 2013--thirteen years after.

Health care and information technology in health care is advancing at tremendous speed. We analysed whether the prognoses by Haux et al. - first presented in 2000 and published in 2002 - have been fulfilled in 2013 and which might be the reasons for match or mismatch. Twenty international experts in biomedical and health informatics met in May 2013 in a workshop to discuss match or mismatch of each of the 71 prognoses. After this meeting a web-based survey among workshop participants took place. Thirty-three prognoses were assessed matching; they reflect e.g. that there is good progress in storing patient data electronically in health care institutions. Twenty-three prognoses were assessed mismatching; they reflect e.g. that telemedicine and home monitoring as well as electronic exchange of patient data between institutions is not established as widespread as expected. Fifteen prognoses were assessed neither matching nor mismatching. ICT tools have considerably influenced health care in the last decade, but in many cases not as far as it was expected by Haux et al. in 2002. In most cases this is not a matter of the availability of technical solutions but of organizational and ethical issues. We need innovative and modern information system architectures which support multiple use of data for patient care as well as for research and reporting and which are able to integrate data from home monitoring into a patient centered health record. Since innovative technology is available the efficient and wide-spread use in health care has to be enabled by systematic information management.

Past and next 10 years of medical informatics.

More than 10 years ago Haux et al. tried to answer the question how health care provision will look like in the year 2013. A follow-up workshop was held in Braunschweig, Germany, for 2 days in May, 2013, with 20 invited international experts in biomedical and health informatics. Among other things it had the objectives to discuss the suggested goals and measures of 2002 and how priorities on MI research in this context should be set from the viewpoint of today. The goals from 2002 are now as up-to-date as they were then. The experts stated that the three goals: "patient-centred recording and use of medical data for cooperative care"; "process-integrated decision support through current medical knowledge" and "comprehensive use of patient data for research and health care reporting" have not been reached yet and are still relevant. A new goal for ICT in health care should be the support of patient centred personalized (individual) medicine. MI as an academic discipline carries out research concerning tools that support health care professionals in their work. This research should be carried out without the pressure that it should lead to systems that are immediately and directly accepted in practice.

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature.

BACKGROUND: Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. RESULTS: We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. CONCLUSIONS: CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems.

e-MIR2: a public online inventory of medical informatics resources.

BACKGROUND: Over the past years, the number of available informatics resources in medicine has grown exponentially. While specific inventories of such resources have already begun to be developed for Bioinformatics (BI), comparable inventories are as yet not available for the Medical Informatics (MI) field, so that locating and accessing them currently remains a difficult and time-consuming task. DESCRIPTION: We have created a repository of MI resources from the scientific literature, providing free access to its contents through a web-based service. We define informatics resources as all those elements that constitute, serve to define or are used by informatics systems, ranging from architectures or development methodologies to terminologies, vocabularies, databases or tools. Relevant information describing the resources is automatically extracted from manuscripts published in top-ranked MI journals. We used a pattern matching approach to detect the resources' names and their main features. Detected resources are classified according to three different criteria: functionality, resource type and domain. To facilitate these tasks, we have built three different classification schemas by following a novel approach based on folksonomies and social tagging. We adopted the terminology most frequently used by MI researchers in their publications to create the concepts and hierarchical relationships belonging to the classification schemas. The classification algorithm identifies the categories associated with resources and annotates them accordingly. The database is then populated with this data after manual curation and validation. CONCLUSIONS: We have created an online repository of MI resources to assist researchers in locating and accessing the most suitable resources to perform specific tasks. The database contains 609 resources at the time of writing and is available at http://www.gib.fi.upm.es/eMIR2. We are continuing to expand the number of available resources by taking into account further publications as well as suggestions from users and resource developers.

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 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.

Using Machine Learning to Collect and Facilitate Remote Access to Biomedical Databases: Development of the Biomedical Database Inventory.

BACKGROUND: Currently, existing biomedical literature repositories do not commonly provide users with specific means to locate and remotely access biomedical databases. OBJECTIVE: To address this issue, we developed the Biomedical Database Inventory (BiDI), a repository linking to biomedical databases automatically extracted from the scientific literature. BiDI provides an index of data resources and a path to access them seamlessly. METHODS: We designed an ensemble of deep learning methods to extract database mentions. To train the system, we annotated a set of 1242 articles that included mentions of database publications. Such a data set was used along with transfer learning techniques to train an ensemble of deep learning natural language processing models targeted at database publication detection. RESULTS: The system obtained an F1 score of 0.929 on database detection, showing high precision and recall values. When applying this model to the PubMed and PubMed Central databases, we identified over 10,000 unique databases. The ensemble model also extracted the weblinks to the reported databases and discarded irrelevant links. For the extraction of weblinks, the model achieved a cross-validated F1 score of 0.908. We show two use cases: one related to "omics" and the other related to the COVID-19 pandemic. CONCLUSIONS: BiDI enables access to biomedical resources over the internet and facilitates data-driven research and other scientific initiatives. The repository is openly available online and will be regularly updated with an automatic text processing pipeline. The approach can be reused to create repositories of different types (ie, biomedical and others).

A review on machine learning approaches and trends in drug discovery.

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

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.

Nanoinformatics and DNA-based computing: catalyzing nanomedicine.

Five decades of research and practical application of computers in biomedicine has given rise to the discipline of medical informatics, which has made many advances in genomic and translational medicine possible. Developments in nanotechnology are opening up the prospects for nanomedicine and regenerative medicine where informatics and DNA computing can become the catalysts enabling health care applications at sub-molecular or atomic scales. Although nanomedicine promises a new exciting frontier for clinical practice and biomedical research, issues involving cost-effectiveness studies, clinical trials and toxicity assays, drug delivery methods, and the implementation of new personalized therapies still remain challenging. Nanoinformatics can accelerate the introduction of nano-related research and applications into clinical practice, leading to an area that could be called "translational nanoinformatics." At the same time, DNA and RNA computing presents an entirely novel paradigm for computation. Nanoinformatics and DNA-based computing are together likely to completely change the way we model and process information in biomedicine and impact the emerging field of nanomedicine most strongly. In this article, we review work in nanoinformatics and DNA (and RNA)-based computing, including applications in nanopediatrics. We analyze their scientific foundations, current research and projects, envisioned applications and potential problems that might arise from them.

BIRI: a new approach for automatically discovering and indexing available public bioinformatics resources from the literature.

BACKGROUND: The rapid evolution of Internet technologies and the collaborative approaches that dominate the field have stimulated the development of numerous bioinformatics resources. To address this new framework, several initiatives have tried to organize these services and resources. In this paper, we present the BioInformatics Resource Inventory (BIRI), a new approach for automatically discovering and indexing available public bioinformatics resources using information extracted from the scientific literature. The index generated can be automatically updated by adding additional manuscripts describing new resources. We have developed web services and applications to test and validate our approach. It has not been designed to replace current indexes but to extend their capabilities with richer functionalities. RESULTS: We developed a web service to provide a set of high-level query primitives to access the index. The web service can be used by third-party web services or web-based applications. To test the web service, we created a pilot web application to access a preliminary knowledge base of resources. We tested our tool using an initial set of 400 abstracts. Almost 90% of the resources described in the abstracts were correctly classified. More than 500 descriptions of functionalities were extracted. CONCLUSION: These experiments suggest the feasibility of our approach for automatically discovering and indexing current and future bioinformatics resources. Given the domain-independent characteristics of this tool, it is currently being applied by the authors in other areas, such as medical nanoinformatics. BIRI is available at http://edelman.dia.fi.upm.es/biri/.

European efforts in nanoinformatics research applied to nanomedicine.

Nanomedicine and nanoinformatics are emerging disciplines with substantial challenges ahead. For instance, nanomedicine involves complex and massive data analysis. Nanoinformatics could expand previous experiences in Biomedical Informatics with new features required to study different scientific biological and physical characteristics at a different level of complexity. ACTION-Grid is a project, funded by the European Commission, which aims to the creation of a collaborative environment in biomedical and nanomedical research among countries in Europe, Western Balkans, Latin America and North Africa. In this paper, we briefly review the concepts of nanomedicine and nanoinformatics and then we describe the activities of some of the ACTION-Grid consortium members considering those initiatives related to nanomedicine.

Nanoinformatics: new challenges for biomedical informatics at the nano level.

Over the last decades Nanotechnology has promised to advance science and technology in many areas. Within medicine, Nanomedicine promises to deliver new methods for diagnosis, prognosis and therapy. As the amount of available information is rapidly growing, new Biomedical Informatics approaches have to be developed to satisfy the increasing demand on data and knowledge management. In 2007, a new sub-discipline, already named "Nanoinformatics", was created with support from the US National Science Foundation. In Europe, a project named ACTION-Grid was launched in 2008 with support from the European Commission to analyze the challenges and agenda for developing Nanoinformatics as a discipline related to Nanotechnology, Biomedicine and Informatics. For MIE 2009, members of this consortium proposed a workshop to discuss the scientific and strategic issues associated with this topic. Nanoinformatics aims to create a bridge between Nanomedicine and Information Technology applying computational methods to manage the information created in the nanomedical domain.

genoDraw: A Web Tool for Developing Pedigree Diagrams Using the Standardized Human Pedigree Nomenclature Integrated with Biomedical Vocabularies.

The integration of genetic information in current clinical routine has raised a need for tools to exploit family genetic knowledge. On the clinical side, an application for managing and visualizing pedigree diagrams could provide genetics specialists with an integrated environment with potential positive impact on their current practice. This article presents a web tool (genoDraw) that provides clinical practitioners with the ability to create, maintain and visualize patients' and their families' information in the form of pedigree diagrams. genoDraw implements a graph-based three-step process for generating diagrams according to a de facto standard in the area and clinical terminologies. It also complies with five characteristics identified as indispensable for the next-generation of pedigree drawing software: comprehensiveness, data-drivenness, automation, interactivity and compatibility with biomedical vocabularies. The platform was implemented and tested, confirming its potential interest to clinical routine.

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 method for indexing biomedical resources over the internet.

A large number of biomedical resources are publicly available over the Internet. This number grows every day. Biomedical researchers face the problem of locating, identifying and selecting the most appropriate resources according to their interests. Some resource indexes can be found in the Internet, but they only provide information and links related to resources created by the owner institution of each website. In this paper we propose a novel method for extracting information from the literature and create a Resourceome, i.e. an index of biomedical resources (databases, tools and services) in a semi-automatic way. In this approach we consider only the information provided by the abstracts of relevant papers in the area. Building a comprehensive resource index is the first step towards the development of new methodologies for the automatic or semi-automatic construction of complex biomedical workflows which allow combining several resources to obtain higher-level functionalities.