Nanoinformatics: a new area of research in nanomedicine

pre-printAbstract: Over a decade ago, nanotechnologists began research on applications of nanomaterials for medicine. This research has revealed a wide range of different challenges, as well as many opportunities. Some of these challenges are strongly related to informatics issues, dealing, for instance, with the management and integration of heterogeneous information, defining nomenclatures, taxonomies and classifications for various types of nanomaterials, and research on new modeling and simulation techniques for nanoparticles. Nanoinformatics has recently emerged in the USA and Europe to address these issues. In this paper, we present a review of nanoinformatics, describing its origins, the problems it addresses, areas of interest, and examples of current research initiatives and informatics resources. We suggest that nanoinformatics could accelerate research and development in nanomedicine, as has occurred in the past in other fields. For instance, biomedical informatics served as a fundamental catalyst for the Human Genome Project, and other genomic and -omics projects, as well as the translational efforts that link resulting molecular-level research to clinical problems and findings

Detectors could spot plagiarism in research proposals

Having all been involved in proposal evaluation, we believe the studies indicate that a text matching analysis of research proposals could reduce plagiarism in subsequent publications. For instance, when European Commission evaluators have met in the past to evaluate research proposals, they received printed copies which had to be returned before the panel members left, and had no computer access during deliberations. A plagiarism detector using text-mining methods could be used instead of the current security measures. Such a system could, in principle, detect similarities to previous submissions and uncited sources using advanced document segmentation. Only official agencies have access to confidential proposals and the funds to experiment with automated plagiarism-detectors. It is important that they should investigate these approaches to reducing the possibility of scientific misconduct

Note on Friedman's "what informatics is and isn't"

Friedman’s article ‘What informatics is and isn’t’, presents a necessary and timely analysis of the field of informatics

A Knowledge Engineering Approach to Recognizing and Extracting Sequences of Nucleic Acids from Scientific Literature

In this paper we present a knowledge engineering approach to automatically recognize and extract genetic sequences from scientific articles. To carry out this task, we use a preliminary recognizer based on a finite state machine to extract all candidate DNA/RNA sequences. The latter are then fed into a knowledge-based system that automatically discards false positives and refines noisy and incorrectly merged sequences. We created the knowledge base by manually analyzing different manuscripts containing genetic sequences. Our approach was evaluated using a test set of 211 full-text articles in PDF format containing 3134 genetic sequences. For such set, we achieved 87.76% precision and 97.70% recall respectively. This method can facilitate different research tasks. These include text mining, information extraction, and information retrieval research dealing with large collections of documents containing genetic sequences

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

Towards Openness in Biomedical Informatics

Over the last years, and particularly in the context of the COMBIOMED network, our biomedical informatics (BMI) group at the Universidad Politecnica de Madrid has carried out several approaches to address a fundamental issue: to facilitate open access and retrieval to BMI resources —including software, databases and services. In this regard, we have followed various directions: a) a text mining-based approach to automatically build a “resourceome”, an inventory of open resources, b) methods for heterogeneous database integration —including clinical, -omics and nanoinformatics sources—; c) creating various services to provide access to different resources to African users and professionals, and d) an approach to facilitate access to open resources from research project

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

[Abstract] 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).Proyecto colaborativo de integración de datos genómicos; PI17/0156

Automatic Generation of Integration and Preprocessing Ontologies for Biomedical Sources in a Distributed Scenario

Access to a large number of remote data sources has boosted research in biomedicine, where different biological and clinical research projects are based on collaborative efforts among international organizations. In this scenario, the authors have developed various methods and tools in the area of database integration, using an ontological approach. This paper describes a method to automatically generate preprocessing structures (ontologies) within an ontology-based KDD model. These ontologies are obtained from the analysis of data sources, searching for: (i) valid numerical ranges (using clustering techniques), (ii) different scales, (iii) synonym transformations based on known dictionaries and (iv)typographical errors. To test the method, experiments were carried out with four biomedical databases―containing rheumatoid arthritis, gene expression patterns, biological processes and breast cancer patients― proving the performance of the approach. This method supports experts in data analysis processes, facilitating the detection of inconsistencies

INFOBIOMED: European Network of Excellence on Biomedical Informatics to support individualised healthcare

INFOBIOMED is an European Network of Excellence (NoE) funded by the Information Society Directorate-General of the European Commission (EC). A consortium of European organizations from ten different countries is involved within the network. Four pilots, all related to linking clinical and genomic information, are being carried out. From an informatics perspective, various challenges, related to data integration and mining, are included

An Automatic Method for Retrieving and Indexing Catalogues of Biomedical Courses

Although there is wide information about Biomedical Informatics education and courses in different Websites, information is usually not exhaustive and difficult to update. We propose a new methodology based on information retrieval techniques for extracting, indexing and retrieving automatically information about educational offers. A web application has been developed to make available such information in an inventory of courses and educational offers