Effective image visualization for publications - a workflow using open access tools and concepts.
Ontology highlight
ABSTRACT:
SUBMITTER: Schmied C
PROVIDER: S-EPMC7931257 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Transparent research in musculoskeletal imaging is fundamental to reliably investigate diseases such as knee osteoarthritis (OA), a chronic disease impairing femoral knee cartilage. To study cartilage degeneration, researchers have developed algorithms to segment femoral knee cartilage from magnetic resonance (MR) images and to measure cartilage morphology and relaxometry. The majority of these algorithms are not publicly available or require advanced programming skills to be compiled and run. However, to accelerate discoveries and findings, it is crucial to have open and reproducible workflows. We present pyKNEEr, a framework for open and reproducible research on femoral knee cartilage from MR images. pyKNEEr is written in python, uses Jupyter notebook as a user interface, and is available on GitHub with a GNU GPLv3 license. It is composed of three modules: 1) image preprocessing to standardize spatial and intensity characteristics; 2) femoral knee cartilage segmentation for intersubject, multimodal, and longitudinal acquisitions; and 3) analysis of cartilage morphology and relaxometry. Each module contains one or more Jupyter notebooks with narrative, code, visualizations, and dependencies to reproduce computational environments. pyKNEEr facilitates transparent image-based research of femoral knee cartilage because of its ease of installation and use, and its versatility for publication and sharing among researchers. Finally, due to its modular structure, pyKNEEr favors code extension and algorithm comparison. We tested our reproducible workflows with experiments that also constitute an example of transparent research with pyKNEEr, and we compared pyKNEEr performances to existing algorithms in literature review visualizations. We provide links to executed notebooks and executable environments for immediate reproducibility of our findings.
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Project description:ObjectiveWe analyzed the COVID-19 Open Research Dataset (CORD-19) to understand leading research institutions, collaborations among institutions, major publication venues, key research concepts, and topics covered by pandemic-related research.MethodsWe conducted a descriptive analysis of authors' institutions and relationships, automatic content extraction of key words and phrases from titles and abstracts, and topic modeling and evolution. Data visualization techniques were applied to present the results of the analysis.ResultsWe found that leading research institutions on COVID-19 included the Chinese Academy of Sciences, the US National Institutes of Health, and the University of California. Research studies mostly involved collaboration among different institutions at national and international levels. In addition to bioRxiv, major publication venues included journals such as The BMJ, PLOS One, Journal of Virology, and The Lancet. Key research concepts included the coronavirus, acute respiratory impairments, health care, and social distancing. The ten most popular topics were identified through topic modeling and included human metapneumovirus and livestock, clinical outcomes of severe patients, and risk factors for higher mortality rate.ConclusionData analytics is a powerful approach for quickly processing and understanding large-scale datasets like CORD-19. This approach could help medical librarians, researchers, and the public understand important characteristics of COVID-19 research and could be applied to the analysis of other large datasets.
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Project description:This paper provides an overview of uncertainty visualization in general, along with specific examples of applications in bioinformatics. Starting from a processing and interaction pipeline of visualization, components are discussed that are relevant for handling and visualizing uncertainty introduced with the original data and at later stages in the pipeline, which shows the importance of making the stages of the pipeline aware of uncertainty and allowing them to propagate uncertainty. We detail concepts and methods for visual mappings of uncertainty, distinguishing between explicit and implict representations of distributions, different ways to show summary statistics, and combined or hybrid visualizations. The basic concepts are illustrated for several examples of graph visualization under uncertainty. Finally, this review paper discusses implications for the visualization of biological data and future research directions.
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Project description:PurposeThe curation of images using human resources is time intensive but an essential step for developing artificial intelligence (AI) algorithms. Our goal was to develop and implement an AI algorithm for image curation in a high-volume setting. We also explored AI tools that will assist in deploying a tiered approach, in which the AI model labels images and flags potential mislabels for human review.DesignImplementation of an AI algorithm.ParticipantsSeven-field stereoscopic images from multiple clinical trials.MethodsThe 7-field stereoscopic image protocol includes 7 pairs of images from various parts of the central retina along with images of the anterior part of the eye. All images were labeled for field number by reading center graders. The model output included classification of the retinal images into 8 field numbers. Probability scores (0-1) were generated to identify misclassified images, with 1 indicating a high probability of a correct label.Main outcome measuresAgreement of AI prediction with grader classification of field number and the use of probability scores to identify mislabeled images.ResultsThe AI model was trained and validated on 17 529 images and tested on 3004 images. The pooled agreement of field numbers between grader classification and the AI model was 88.3% (kappa, 0.87). The pooled mean probability score was 0.97 (standard deviation [SD], 0.08) for images for which the graders agreed with the AI-generated labels and 0.77 (SD, 0.19) for images for which the graders disagreed with the AI-generated labels (P < 0.0001). Using receiver operating characteristic curves, a probability score of 0.99 was identified as a cutoff for distinguishing mislabeled images. A tiered workflow using a probability score of < 0.99 as a cutoff would include 27.6% of the 3004 images for human review and reduce the error rate from 11.7% to 1.5%.ConclusionsThe implementation of AI algorithms requires measures in addition to model validation. Tools to flag potential errors in the labels generated by AI models will reduce inaccuracies, increase trust in the system, and provide data for continuous model development.
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Project description:BACKGROUND: We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible. RESULTS: We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets. CONCLUSIONS: TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool.
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Project description:The COVID-19 pandemic is creating a global health emergency. Mapping this health emergency in scientific publications demands multiple approaches to obtain a picture as complete as possible. To progress in the knowledge of this pandemic and to control its effects, international collaborations between researchers are essentials, as well as having open and immediate access to scientific publications, what we called "coopetition". Our main objectives are to identify the most productive countries in coronavirus publications, to analyse the international scientific collaboration on this topic, and to study the proportion and typology of open accessibility to these publications. We have analyzed 18,875 articles indexed in Web of Science. We performed the descriptive statistical analysis in order to explore the performance of the more prolific countries and organizations, as well as paying attention to the last 2 years. Registers have been analyzed separately via the VOSviewer software, drawing a network of links among countries and organizations to identify the starred countries and organizations, and the strongest links of the net. We have explored the capacity of researchers to generate scientific knowledge about a health crisis emergency, and their global capacity to collaborate among them in a global emergency. We consider that science is moving rapidly to find solutions to international health problems but access to this knowledge by society is not so quick due to several limitations (open access policies, corporate interests, etc.). We have observed that papers from China in the last 3 months (from January 2020 to March 2020) have a strong impact compared with papers published in years before. The United States and China are the major producers of documents of our sample, followed by all European countries, especially the United Kingdom, Germany, the Netherlands, and France. At the same time, the leading role of Saudi Arabia, Canada or South Korea should be noted, with a significant number of documents submitted but very different dynamics of international collaboration. The proportion of international collaboration is growing in all countries in 2019-2020, which contrasts with the situation of the last two decades. The organizations providing the most documents to the sample are mostly Chinese. The percentage of open access articles on coronavirus for the period 2001-2020 is 59.2% but if we focus in 2020 the figures increase up to 91.4%, due to the commitment of commercial publishers with the emergency.
| S-EPMC7328881 | biostudies-literature
Project description:Background: The COVID-19 outbreak has made funders, researchers and publishers agree to have research publications, as well as other research outputs, such as data, become openly available. In this extraordinary research context of the SARS CoV-2 pandemic, publishers are announcing that their coronavirus-related articles will be made immediately accessible in appropriate open repositories, like PubMed Central (PMC), agreeing upon funders' and researchers' instigation. Methods: This work uses Unpaywall, OpenRefine and PubMed to analyse the level of openness of the papers about COVID-19, published during the first quarter of 2020. It also analyses Open Access (OA) articles published about previous coronavirus (SARS CoV-1 and MERS CoV) as a means of comparison. Results: A total of 5,611 COVID-19-related articles were analysed from PubMed. This is a much higher amount for a period of 4 months compared to those found for SARS CoV-1 and MERS during the first year of their first outbreaks (337 and 125 articles, respectively). Regarding the levels of openness, 97.4% of the SARS CoV-2 papers are freely available; similar rates were found for the other coronaviruses. Deeper analysis showed that (i) 68.3% of articles belong to an undefined Bronze category; (ii) 72.1% of all OA papers don't carry a specific license and in all cases where there is, half of them do not meet Open Access standards; (iii) there is a large proportion that present a copy in a repository, in most cases in PMC, where this trend is also observed. These patterns were found to be repeated in most frequent publishers: Elsevier, Springer and Wiley. Conclusions: Our results suggest that, although scientific production is much higher than during previous epidemics and is open, there is a caveat to this opening, characterized by the absence of fundamental elements and values on which Open Science is based, such as licensing.
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