Recent trends in biomedical informatics: a study based on JAMIA articles.
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ABSTRACT:
SUBMITTER: Jiang X
PROVIDER: S-EPMC3861936 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Three-dimensional (3D) bioprinting is an appealing and revolutionary manufacturing approach for the accurate placement of biologics, such as living cells and extracellular matrix (ECM) components, in the form of a 3D hierarchical structure to fabricate synthetic multicellular tissues. Many synthetic and natural polymers are applied as cell printing bioinks. One of them, alginate (Alg), is an inexpensive biomaterial that is among the most examined hydrogel materials intended for vascular, cartilage, and bone tissue printing. It has also been studied pertaining to the liver, kidney, and skin, due to its excellent cell response and flexible gelation preparation through divalent ions including calcium. Nevertheless, Alg hydrogels possess certain negative aspects, including weak mechanical characteristics, poor printability, poor structural stability, and poor cell attachment, which may restrict its usage along with the 3D printing approach to prepare artificial tissue. In this review paper, we prepare the accessible materials to be able to encourage and boost new Alg-based bioink formulations with superior characteristics for upcoming purposes in drug delivery systems. Moreover, the major outcomes are discussed, and the outstanding concerns regarding this area and the scope for upcoming examination are outlined.
| S-EPMC7557490 | biostudies-literature
Project description:ObjectiveBiomedical informatics attracts few underrepresented racial minorities (URMs) into PhD programs. We examine graduation trends from 2002 to 2017 to determine how URM representation has changed over time. We also examine academic job placements by race and identify individual and institutional characteristics associated with URM graduates being successfully placed in academic jobs.Materials and methodsWe analyze a near census of all research doctoral graduates from US-accredited institutions, surveyed at graduation by the National Science Foundation Survey of Earned Doctorates. Graduates of biomedical informatics-related programs were identified using self-reported primary and secondary disciplines. Data are analyzed using bivariate and multivariable logistic regressions.ResultsDuring the study period, 2426 individuals earned doctoral degrees in biomedical informatics-related disciplines. URM students comprised nearly 12% of graduates, and this proportion did not change over time (2002-2017). URMs included Hispanic (5.7%), Black (3.2%), and others, including multi-racial and indigenous American populations (2.8%). Overall, 82.3% of all graduates accepted academic positions at the time of graduation with significantly more Hispanic graduates electing to go into academia (89.2%; P < .001). URM graduates were more likely to be single (OR = 1.38; P < .05), have a dependent (1.95; P < .01), and not receive full tuition remission (OR = 1.37; P = .05) as a student. URM graduates accepting an academic position were less likely to be a graduate of a private institution (OR = 0.70; P < .05).Discussion and conclusionThe proportion of URM candidates among biomedical informatics doctoral graduates has not increased over time and remains low. In order to improve URM recruitment and retention within academia, leaders in biomedical informatics should replicate strategies used to improve URM graduation rates in other fields.
| S-EPMC7671637 | biostudies-literature
Project description:Biomedical literature is an essential source of biomedical evidence. To translate the evidence for biomedicine study, researchers often need to carefully read multiple articles about specific biomedical issues. These articles thus need to be highly related to each other. They should share similar core contents, including research goals, methods, and findings. However, given an article r, it is challenging for search engines to retrieve highly related articles for r. In this paper, we present a technique PBC (Passage-based Bibliographic Coupling) that estimates inter-article similarity by seamlessly integrating bibliographic coupling with the information collected from context passages around important out-link citations (references) in each article. Empirical evaluation shows that PBC can significantly improve the retrieval of those articles that biomedical experts believe to be highly related to specific articles about gene-disease associations. PBC can thus be used to improve search engines in retrieving the highly related articles for any given article r, even when r is cited by very few (or even no) articles. The contribution is essential for those researchers and text mining systems that aim at cross-validating the evidence about specific gene-disease associations.
| S-EPMC4595445 | biostudies-literature
Project description:ObjectivesTo examine roles for summer internship programs in expanding pathways into biomedical informatics, based on 10 years of the Vanderbilt Department of Biomedical Informatics (DBMI) Summer Research Internship Program.Materials and methodsVanderbilt DBMI's internship program is a research-intensive paid 8-10 week program for high school, undergraduate, and graduate students. The program is grounded in a "Windows, Mirrors, and Open Doors" educational framework, and is guided by an evolving set of design principles, including providing meaningful research experiences, applying a multi-factor approach to diversity, and helping interns build peer connections.ResultsOver 10 years, 90 individuals have participated in the internship program, with nine students participating for more than one summer. Of 90 participants, 38 were women and 52 were men. Participants represented a range of racial/ethnic groups. A total of 39 faculty members have served as primary mentor for one or more interns. Five key lessons emerged from our program experience: Festina Lente ("Make haste slowly"), The Power of Community, Learning by Doing, Thoughtful Partnerships Lead to Innovation, and The Whole is More Than the Sum of Its Parts.DiscussionBased on our experience, we suggest that internship programs should become a core element of the biomedical informatics educational ecosystem. Continued development and growth of this important educational outreach approach requires stable funding sources and building connections between programs to share best practices.ConclusionInternship programs can play a substantial role in the biomedical informatics educational ecosystem, helping introduce individuals to the field earlier in their educational trajectories.
| S-EPMC6951956 | biostudies-literature
Project description:Molecular biology and literature databases represent essential infrastructure for life science research. Effective integration of these data resources requires that there are structured cross-references at the level of individual articles and biological records. Here, we describe the current patterns of how database entries are cited in research articles, based on analysis of the full text Open Access articles available from Europe PMC. Focusing on citation of entries in the European Nucleotide Archive (ENA), UniProt and Protein Data Bank, Europe (PDBe), we demonstrate that text mining doubles the number of structured annotations of database record citations supplied in journal articles by publishers. Many thousands of new literature-database relationships are found by text mining, since these relationships are also not present in the set of articles cited by database records. We recommend that structured annotation of database records in articles is extended to other databases, such as ArrayExpress and Pfam, entries from which are also cited widely in the literature. The very high precision and high-throughput of this text-mining pipeline makes this activity possible both accurately and at low cost, which will allow the development of new integrated data services.
| S-EPMC3667078 | biostudies-literature
Project description:ObjectiveThe field of Biomedical and Health Informatics (BMHI) continues to define itself, and there are many educational programs offering 'informatics' degrees with varied foci. The goal of this study was to develop a scheme for systematic comparison of programs across the entire BMHI spectrum and to identify commonalities among informatics curricula.DesignGuided by several published competency sets, a grounded theory approach was used to develop a program/curricula categorization scheme based on the descriptions of 636 courses offered by 73 public health, nursing, health, medical, and bioinformatics programs in the USA. The scheme was then used to compare the programs in the aforementioned five informatics disciplines.ResultsThe authors developed a Course-Based Informatics Program Categorization (CBIPC) scheme that can be used both to classify coursework for any BMHI educational program and to compare programs from the same or related disciplines. The application of CBIPC scheme to the analysis of public health, nursing, health, medical, and bioinformatics programs reveals distinct intradisciplinary curricular patterns and a common core of courses across the entire BMHI education domain. Limitations The study is based on descriptions of courses from the university's webpages. Thus, it is limited to sampling courses at one moment in time, and classification for the coding scheme is based primarily on course titles and course descriptions.ConclusionThe CBIPC scheme combines empirical data about educational curricula from diverse informatics programs and several published competency sets. It also provides a foundation for discussion of BMHI education as a whole and can help define subdisciplinary competencies.
| S-EPMC3116256 | biostudies-literature
Project description:BackgroundConclusive association entities (CAEs) in a biomedical article a are those biomedical entities (e.g., genes, diseases, and chemicals) that are specifically involved in the associations concluded in a. Identification of CAEs among candidate entities in the title and the abstract of an article is essential for curation and exploration of conclusive findings in biomedical literature. However, the identification is challenging, as it is difficult to conduct semantic analysis to determine whether an entity is a specific target on which the reported findings are conclusive enough.ResultsWe investigate how five types of statistical indicators can contribute to prioritizing the candidate entities so that CAEs can be ranked on the top for exploratory analysis. The indicators work on titles and abstracts of articles. They are evaluated by the CAEs designated by biomedical experts to curate entity associations concluded in articles. The indicators have significantly different performance in ranking the CAEs identified by the biomedical experts. Some indicators do not perform well in CAE identification, even though they were used in many techniques for article retrieval and keyword extraction. Learning-based fusion of certain indicators can further improve performance. Most of the articles have at least one of their CAEs successfully ranked at top-2 positions. The CAEs can be visualized to support exploratory analysis of conclusive results on the CAEs.ConclusionWith proper fusion of the statistical indicators, CAEs in biomedical articles can be identified for exploratory analysis. The results are essential for the indexing of biomedical articles to support validation of highly related conclusive findings in biomedical literature.
| S-EPMC6322258 | biostudies-literature
Project description:The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical data sets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 data set consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 T scanners. The FBIRN Phase 2 and Phase 3 data sets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data.
| S-EPMC4651841 | biostudies-literature
Project description:MotivationMany researchers with domain expertise are unable to easily apply machine learning (ML) to their bioinformatics data due to a lack of ML and/or coding expertise. Methods that have been proposed thus far to automate ML mostly require programming experience as well as expert knowledge to tune and apply the algorithms correctly. Here, we study a method of automating biomedical data science using a web-based AI platform to recommend model choices and conduct experiments. We have two goals in mind: first, to make it easy to construct sophisticated models of biomedical processes; and second, to provide a fully automated AI agent that can choose and conduct promising experiments for the user, based on the user's experiments as well as prior knowledge. To validate this framework, we conduct an experiment on 165 classification problems, comparing to state-of-the-art, automated approaches. Finally, we use this tool to develop predictive models of septic shock in critical care patients.ResultsWe find that matrix factorization-based recommendation systems outperform metalearning methods for automating ML. This result mirrors the results of earlier recommender systems research in other domains. The proposed AI is competitive with state-of-the-art automated ML methods in terms of choosing optimal algorithm configurations for datasets. In our application to prediction of septic shock, the AI-driven analysis produces a competent ML model (AUROC 0.85±0.02) that performs on par with state-of-the-art deep learning results for this task, with much less computational effort.Availability and implementationPennAI is available free of charge and open-source. It is distributed under the GNU public license (GPL) version 3.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8055228 | biostudies-literature
Project description:The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn the generative model of any data distribution through adversarial methods with excellent performance. It has been widely applied to different areas since it was proposed in 2014. In this review, we introduced the origin, specific working principle, and development history of GAN, various applications of GAN in digital image processing, Cycle-GAN, and its application in medical imaging analysis, as well as the latest applications of GAN in medical informatics and bioinformatics.
| S-EPMC7235323 | biostudies-literature