Machine learning Archives

Novelty in life science: Looking into the unseen

In life science research, navigating the complexities of innovation is crucial for breakthroughs. SciBite’s Novelty model, a sophisticated Machine Learning classifier, distinguishes true innovation in scientific texts.
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AI based chat application for life sciences:
Part I key considerations

Are your teams now posing potentially confidential questions to consumer tools such as Bard and ChatGPT, relying on their responses?
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The powerful combination of semantics-based Machine Learning and domain expertise

In this blog hear about SciBite's recent talk at Pistoia Alliance’s Spring Virtual Conference on semantics-based machine learning and domain expertise on a day dedicated to emerging science and technologies.
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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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Machine Learning insights from Japanese language academic text

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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Semantic approach to training ML data sets using ontologies & not Sherlock Holmes

In this blog we 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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Building the future of text analytics

SciBite CSO and Founder Lee Harland features in KM World Magazine, where he talks about 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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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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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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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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Why simplifying visualization 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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The benefits of centralizing 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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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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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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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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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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Machine Learning and phenotype triangulation

Disease detective part 3: 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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