Elsevier BV

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  • Data about fruit eating habits and home types. Making this bit better. New version containing even more fruit.
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  • My data
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  • ST1_HW_ZSVPN_NMLGU_16Feb2021(DT1.1)
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  • -> The Human Disease Author Aid Collection combines information about rare and common diseases in standardized, easy-to-navigate overviews and tables. -> The Author Aid Collection includes clinical, molecular, and pharmacological data from several Elsevier and public sources. -> Author Aid Templates can be a helpful guide for authors, researchers, clinicians, and students, especially those interested in rare diseases, because they highlight the latest updates, findings, and basic disease information from several sources on one page. Contents -> The Human Disease Author Aid Collection is published in parts with 5-10 diseases grouped by therapeutic areas, except Part 1. Tables are planned to be updated with the latest metadata and citations quarterly. -> Part 1 includes opening examples for common and rare human diseases: hemophilia, phenylketonuria, alpha-1 antitrypsin deficiency, migraine, and COVID-19. -> Each disease template overview in Part 1 includes six sections: Terminology; Epidemiology/Demographics; Clinical presentation/Diagnosis; Etiology/Pathology (genetics, biomarkers, pathways); Treatment/Follow-Up; and Case studies. Each subset of data is linked to a list of publications with relevant citations.
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  • This table provides a summary of the characteristics of 18 pediatric patients with alopecia areata and concomitant atopic dermatitis who have been prescribed dupilumab.
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  • Data matching research publications to the UN SDGs via the RELX corporate responsibility platform https://sdgresources.relx.com/match-research-to-sdgs
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  • This is the dataset supporting the publication Discovering Gene-Disease Associations with Biomedical Word Embeddings. Finding the right target for a disease is critical in the drug development process. This paper presents a machine learning approach for predicting gene-disease associations that (i) employs biomedical word embeddings as features for a classifier trained on Open Targets Platform (OTP) data that (ii) generalises beyond a specific disease or gene class. We train, evaluate and compare different word embedding models and classifiers for the task at hand. In addition, we validate the approach by training on a past OTP release and show that it can assist in identifying probable positive associations among current low evidence associations, confirmed by a recent OTP release. Furthermore, we train word embedding models on different time slices of biomedical articles from ScienceDirect and demonstrate that the trained classifier predicts associations that have not explicitly been mentioned in the training corpus, 5 years into the future. Please send a message to Elsevier describing briefly your request on how you would like to use the assets with a short justification. Elsevier will connect directly with you for the elaboration of a personalized license. The contact information can be found in the license information.
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  • Access requests approved by author
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  • queue-vs-topic v4
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