Articles tagged: Machine Learning

  1. SciBite CTO James Malone joins ISB Panel to discuss AI and the Future of Biocuration

    Our very own SciBite CTO James Malone was invited to take part in a panel discussion as part of this year's virtual Biocuration Conference, where he shared his thoughts in a thought-provoking discussion on “The Future of Biocuration.”

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  2. SciBite launches SciBiteAI Relationship Extraction models

    SciBite announces the release of SciBiteAI Relationship Extraction models, which provide the enhanced ability to identify complex relationships within text to further unlock insights from Life Sciences data.

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  3. A Helping Hand: Annotation of the COVID-19 Open Research Dataset for the Scientific Research Community

    In this blog find out how the SciBite team has responded to the tech community call to arms from The White House after they released an Open Research Dataset (CORD-19), with the hope to help uncover insights and answer high-priority scientific questions related to COVID-19.

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  4. SciBite launches state-of-the-art AI platform for Life Sciences organisations

    SciBite announces the launch of SciBiteAI, a state-of-the-art Artificial Intelligence software platform for leveraging machine learning models alongside semantic technologies to unlock insights into Life Sciences data.

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  5. Unlocking insight from scientific literature in under-supported languages through Machine Learning approaches

    In this blog, we delve into how we applied novel machine learning and curation methods to Japanese language literature, techniques we believe are transferable to other under-supported languages.

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  6. The 12 days of Machine Learning tips for creating labelled training data

    On the first day of Christmas SciBite gave to me... 12 top tips for creating labelled Machine Learning training data.

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  7. A Semantic approach to creating Machine Learning training data using Ontologies, Wikipedia and not Sherlock Holmes

    SciBite's CTO James Malone explains how the semantic approach to using ontologies is essential in successfully training machine learning data sets. In this blog he discusses how Sherlock Holmes (amongst others) made an appearance when we looked to exploit the efforts of Wikipedia to identify articles relevant to the life science domain for a language model project.

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  8. Human and Machine Intelligence: Building the future of Text Analytics

    SciBite CSO and Founder Lee Harland features in KM World Magazine where he talks on the future of text analytics and how ontologies are the de facto standard to encode semantics in an understandable form for both humans and machines.

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  9. A helping hand from BERT for Deep Learning approaches

    SciBite CSO and Founder Lee Harland shares his views on the use of BERT (or more specifically BioBERT) for deep learning approaches.

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  10. Ontologies with SciBite: How Ontology enrichment is essential in maintaining Clean Data

    Ontologies have become a key piece of infrastructure for organisations as they look to manage their metadata to improve the reusability and findability of their data. This is the final blog in our blog series 'Ontologies with SciBite'. Follow the blog series to learn how we've addressed the challenges associated with both consuming and developing ontologies.

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  11. Ontologies with SciBite: The importance of facilitating collaboration and integration

    Ontologies have become a key piece of infrastructure for organisations as they look to manage their metadata to improve the reusability and findability of their data. This is the third blog in our blog series 'Ontologies with SciBite'. Follow the blog series to learn how we've addressed the challenges associated with both consuming and developing ontologies.

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  12. Ontologies with SciBite: Why simplifying visualisation and curation is better for everyone

    Ontologies have become a key piece of infrastructure for organisations as they look to manage their metadata to improve the reusability and findability of their data. This is the second blog in our blog series 'Ontologies with SciBite'. Follow the blog series to learn how we've addressed the challenges associated with both consuming and developing ontologies.

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  13. Ontologies with SciBite: The benefits of centralising Ontology Management

    Ontologies have become a key piece of infrastructure for organisations as they look to manage their metadata to improve the reusability and findability of their data. This is the first blog in our blog series 'Ontologies with SciBite'. Follow the blog series to learn how we've addressed the challenges associated with both consuming and developing ontologies.

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  14. How the use of Machine Learning can augment Adverse Event detection

    When it comes to identifying adverse events (AEs), things are not always as they seem. Consider a paper describing a new treatment for a given illness - how can we determine which adverse event terms refer to actual adverse events as opposed to symptoms of the illness itself, given that those terms may be identical? Is this new drug treating arrhythmias or causing them, for example?

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  15. The SciBite difference – It’s all about your data

    In this blog post SciBite's CSO and Founder Lee Harland addresses a very common question we are often asked by potential customers and partners...

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  16. SciBite announced as Best of Show Award finalists for Bio-IT World 2019

    SciBite has been shortlisted for Bio-IT World 2019’s prestigious Best of Show Award.

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  17. Are Ontologies relevant in a Machine Learning-centric world?

    SciBite CSO and Founder Lee Harland shares his views on why ontologies are relevant in a machine learning-centric world and are essential to help "clean up" scientific data in the Life Sciences industry.

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  18. Of Burns and Bums: Machine Learning surprises!

    As many of our regular visitors will know, the focus of our work here at SciBite is unlocking the knowledge held in the vast amount of biomedical text researchers have access to. Sometimes this yields well, interesting, results.....

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  19. Disease Detective Part 3: Machine Learning and phenotype triangulation

    In our final disease detective article, we’ll take Part 2’s topic a little further and zoom in on how we can find new relationships between diseases where direct evidence is sparse.

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