AI Archives - SciBite

Large language models (LLMs) and search; it’s a FAIR game

Large language models (LLMs) have limitations when applied to search due to their inability to distinguish between fact and fiction, potential privacy concerns, and provenance issues. LLMs can, however, support search when used in conjunction with FAIR data and could even support the democratisation of data, if used correctly…
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A Report from the Biocuration 2023 Conference in Padua, Italy

The Biocuration Conference this year was held in the beautiful historic town of Padua in the Veneto region of Italy, renowned for its ancient University and picturesque old town. The stylish and relaxed atmosphere was the perfect place for catching up with old friends and establishing new connections and collaborations.
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A review of the Pistoia Alliance Spring Conference 2023

Last week SciBite was lucky enough to attend, and present at, the Pistoia Alliance Annual Spring Conference ‘23, held at the fantastic Leonardo Royal Hotel, St. Pauls, London. Read the thoughts of our Director of Technical Consultants, Joe Mullen
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Training AI is hard, so we trained an AI to do it

GPT3 (which stands for 'generative pretrained transformer 3') is a large language model that is capable of generating text with very high fidelity. Unlike previous models, it doesn't stumble over its grammar or write like an inebriated caveman. In many circumstances it can easily be taken for a human author, and GPT-generated text is increasingly prolific across the internet (and, we suspect, in the classroom) for this reason.
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Revolutionizing Life Sciences: The incredible impact of AI in Life Science [Part 2]

As discussed in part 1, Artificial intelligence (AI) has revolutionized several areas in life sciences, including disease diagnosis and drug discovery. In this second blog, we introduce some specific text-based models whilst also discussing the challenges and future impact of AI in Life Science.
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Matching patients to clinical trials

Patient X, suffering from an untreatable gastrointestinal disease, chats with a large language model called GPT for advice. GPT suggests looking at clinical trials and Patient X finds 10 active recruiting trials but is unsure which to choose. Patient X consults his doctor who recommends a trial from a pharmaceutical company. What could go wrong?
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Revolutionizing Life Sciences: The incredible impact of AI in Life Science [Part 1]

Artificial intelligence (AI) has been applied to numerous aspects of the life sciences, from disease diagnosis to drug discovery; in the first of this two-part blog series, we outline the impact of AI in Life Science and illustrate the various success stories of AI in Life Science.
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Detecting adverse drug reactions in social media posts 

The highly informal language used in social media posts is challenging to analyse. Recognizing this need, SciBite has created a machine learning-based model capable of identifying adverse Drug Reactions associated with medications from the informal language found in social media posts.
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SciBite Virtual User Group Meeting 2021:
What we discovered

SciBite, the award-winning semantic technology company, recently hosted its annual User Group Meeting virtually for the second year running. Held over three days, we brought together our customers and partners to have discussions around Ontologies and Ontology-Powered Entity Registration, FAIR data ecosystems, Data FAIRification, and Knowledge Graphs
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SciBite announces the release of SciBite Search 2.0

In this blog we announce the v2.0 release of SciBite Search, our intelligent scientific search platform. We’ve expanded our Elsevier data connectivity, broadening the sources you can load and search, as well as a host of features that improve the user experience.
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SciBite AI 2.0: Combine deep learning models with powerful semantic algorithms

SciBite AI combines deep learning Artificial Intelligence models with our powerful semantic algorithms, enabling the pharmaceutical and healthcare sectors to exploit and rapidly use life science data in research and development.
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A de novo vocabulary approach for producing Machine Learning models (AI Models)

In this blog hear how our SciBite AI team demonstrated a de novo vocabulary approach for generating a machine learning model, allowing researchers to identify and annotate text containing mutant descriptors.
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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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SciBite joins ISB panel to discuss AI and the Future of Biocuration

Our SciBite CTO 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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SciBite launches AI-driven semantic search platform to help manage data deluge

Intelligent scientific search platform, SciBite Search, enables researchers to quickly find meaningful insights from structured and unstructured public and proprietary biomedical data.
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SciBite launches SciBite AI relationship extraction models

SciBite announces the release of SciBite AI 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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SciBite showcases cutting-edge insights to life science leaders

Cambridge, UK – SciBite, the award-winning semantic technology company, recently hosted its first Virtual User Group Meeting across three days, bringing together its global customer base to share cutting-edge technology insights with life science leaders.
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Launch of Artificial Intelligence software platform for Life Sciences organisations

SciBite announces the launch of SciBite AI, 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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Semantic approach to training ML data sets using ontologies & not Sherlock Holmes

SciBite's CTO 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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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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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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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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Climbing Mt Peer Review: May AI help you with that?

At every stage of a researcher’s career, literary reviews are an integral part of scientific investigation. They help to ensure researchers are aware of the current landscape, latest findings and newest technologies in their field.
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Microbiome analyzer supports IBD patients at home

The work presented here describes the winning entry to a challenge set by ARM, Atos and Cavium at the Wellcome Genome Campus Hackathon 2018.
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Text mining drug labels for genetic factors influencing efficacy and safety

Only 50-75% of patients respond beneficially to first-line drug therapy, causing unnecessary costs to healthcare providers and, more importantly, adversely impacting a patient’s quality of life. Text mining drug labels for genetic factors influencing efficacy and safety to support clinicians at the point of prescription.
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SciBite technology transforms data mining & horizon scanning

With 6,800 new scientific publications released every day (one every 12 seconds) data mining and horizon scanning is becoming increasingly difficult for medical researchers, which can lead to delayed discoveries in the life science space.
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