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We help you manage, find and share your data with digital tools that support research and unlock discovery.Learn more
|Date / Time|
|PEPSI-KOLA: A Data-Driven Approach to Find and Connect with Thought Leaders
|Rishi Gupta, Principal Research Scientist, Data Science and Informatics at AbbVie||Tuesday 28th April 2020 | 3pm BST | 10am EDT | 7am PDT|
|Moving Faster with Sinequa & SciBite -
Unlocking Insight from Medical Publications
Partnership Manager at SciBite
Gengis Birsen, Sr. Sales Engineer at Sinequa
|Tuesday 28th April 2020 | 4pm BST | 11am EDT | 8am PDT|
|Creating Knowledge Graphs from Literature
Lead Technical Consultant at SciBite
|Wednesday 29th April 2020 | 3pm BST | 10am EDT | 7am PDT|
|Applications for Centralised, Enterprise-Ready Ontology Management
Product Manager at SciBite
|Thursday 30th April 2020 | 2pm BST | 9am EDT | 6am PDT|
Our technology is trusted by businesses across the globe including the top 20 pharma to unlock the potential of their scientific data making it a critical component in scientific data-led strategies.Read more
Most pharmaceutical companies will have, at some point, deployed an Electronic Laboratory Notebook (ELN) with the goal of centralising R&D data. ELNs have become an important source of both key experimental results and the development history of new methods and processes.
However, most pharmaceutical companies are unable to realise the true value of the data stored in their ELN. Much of the information stored within it is captured as qualitative free text or as attachments, with the ability to mine it limited to rudimentary text and keyword searches.
SciBite’s unique combination of retrospective and prospective semantic enrichment immediately brings scientifically intelligent search to any ELN platform, opening up new possibilities to mine the data more effectively and derive valuable scientific and business insights.
The struggle to effectively utilise the increasing volumes of data available is a common challenge in the Life Sciences research industry. Artificial Intelligence (AI) is frequently touted as a potential solution to extract valuable insights from large volumes of heterogeneous data. However, tangible successes to date have been relatively few.
Areas bearing the greatest demonstrable success often utilise machine learning (ML), yet even these are at the mercy of the quality of the source data. Scientifically naive systems struggle to deal with the complexity and variability of unstructured scientific language. In a recent survey of over 16,700 data scientists, the most commonly cited challenge to undertaking machine learning was “dirty data”.
SciBite harmonises data by exploiting ontologies to automate semantic enrichment and annotation, whilst also coping with ambiguities such as synonyms, typographic errors or cryptic data, such as project codes, cell line IDs, and internal drug abbreviations.
Regulatory bodies expect pharmaceutical companies to maintain an up-to-date awareness of the safety implications of not only their own drugs but also those from the same drug class and with the same target that are marketed by competitors. Comprehensive, systematic monitoring is required in order to detect, validate and act upon new adverse events as early as possible.
This places significant demands on Pharmacovigilance teams, who are challenged to maintain safety and compliance amid increasingly stringent, globally diverse regulations. The legacy approach, involving manually scanning biomedical sources, is prohibitively time consuming, has a high risk of missing safety signals and is no longer a viable option.
SciBite provides a resource-effective solution to the challenges faced by Pharmacovigilance teams by unlocking the potential of unstructured biomedical content. With SciBite, pharmaceutical companies can monitor a wide range of heterogeneous and cross-disciplinary sources and reach timely, well-informed decisions, resulting in safer treatments for patients.
In this presentation, you'll hear from GSK's Scientific Lead within the Data and Computational Sciences Solutions team, Samiul Hasan, and SciBite's Head of Data Science, Michael Hughes, on how semantic integration can be made to ultimately become part of an integrated learning framework for more informed scientific decision making. They will take you through our pilot experiment and highlight practical learnings that should inform subsequent endeavours.
According to Forrester Research, "Relevant knowledge at the right time is priceless in the enterprise." This is particularly true in the highly competitive pharma industry where knowledge is hidden in a deluge of structured and unstructured data of all kinds, buried among publicly available sources such as trade databases, scientific publications, and patents, as well as internal repositories like R&D data, clinical trials, recorded patient interviews, and more!
During this webinar, hear how Sinequa and SciBite have teamed up to provide a winning combination of Cognitive Search and powerful Life Sciences Semantics to help leading pharma organizations unlock the wealth of R&D data.
At a time where more and more of our customer projects revolve around knowledge graph creation, we thought it was about time we blogged on what exactly a knowledge graph is and explain a bit more about how our semantic enrichment technology is being used to facilitate the production of such a powerful data model.Read
Here 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.Read
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