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  • Author: Sebastian Schmidt, Sabine Schefzick
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This talk from the Pistoia Alliance Spring Conference summarizes Pfizer's Knowledge Graph journey and highlights their lessons learned. By leveraging Knowledge Graph technology to make R&D data FAIR, Pfizer can surface actionable and meaningful insights and make knowledge accessible to and consumable by domain users. Built on top of metaphactory - a low-code Knowledge Graph platform, Pfizer's SCAI Dash (Science & Clinical Analytics and Informatics Dashboard) helps accelerate R&D data discovery and understanding. It supports data scientists, researchers, scientists, and clinical and project leads in bringing medicine to the market faster.

 

Featured Topics

  • Making knowledge accessible and consumable to domain users through an application powered by Knowledge Graphs utilizing FAIR Data Principles
  • Building an intuitive and interactive end-user interface for exploration and discovery
  • Showcasing the power of Knowledge Graphs for bridging the gap between research and development

Learning Objectives

At the conclusion of this session, participants should be able to:

  • Plan their next steps for their Knowledge Graph journey to accelerate R&D data discovery
  • Define an iterative and agile process for building a user experience that enables collaboration and adjusts as their Knowledge Graph grows from one to many use cases
  • Demonstrate the power of Knowledge Graphs to decisions makers to gain traction/buy-in for their initiatives

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  • Author: Sebastian Schmidt, Ilian Uzunov, Todor Primov
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In this webinar, metaphacts and Ontotext highlight the benefits of knowledge graphs for driving digital transformation in the Life Sciences & Pharma industry and share important lessons learned from numerous customer implementations.

Webinar overview

To address topics such as precision medicine, full DNA sequencing or digital therapeutics, Life Sciences and Pharma companies need to accelerate their digital transformation journeys and move towards data fabrics built on FAIR Data that offer a modular approach to data integration and knowledge consumption.

In this webinar, metaphacts and Ontotext highlight the benefits of knowledge graphs for driving digital transformation in the Life Sciences & Pharma industry and share important lessons learned from numerous customer implementations. We use examples from a pre-clinical knowledge graph capturing OMICs data (genomics, proteomics, transcriptomics) together with information about pathways, tissues, drugs and diseases. The knowledge graph used in the webinar is built entirely from publicly available datasets, which are semantically mapped to each other and against the common data model. These public datasets include ChEMBL, Ensembl, UniProt, GWAS Catalog and Reactome. We also outline the process of adding your own data (e.g., in the form of expression data) to the mix.

Such a comprehensive pre-clinical knowledge graph provides highly interlinked information across these various domains, delivering the needed insights, e.g., how to identify targets for new drugs through protein interaction partners or along the pathway.

The webinar also discusses how a knowledge graph can help answer very specific requests such as: "Give me all interaction partners on human proteins, which are targeted by drugs used for the treatment of endometrial neoplasm, and which can be found in tissue type Uterus and are manually reviewed". Additionally, the presentation will include details on how this embeds into the users' (e.g., bioinformaticians, chemists, etc.) existing toolchains and how data can be imported and exported from the knowledge graph to use in existing, domain-specific tools.

Watch the webinar recording here »

Presenters

Sebastian Schmidt - Co-CEO, metaphacts
Ilian Uzunov - Sales Director Life Sciences and Healthcare, Ontotext
Todor Primov - Head of Research and Innovation, Ontotext

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This webinar features a solution developed by Fresh Gravity for scoping clinical trials using metaphactory and Stardog. It explains how knowledge graphs can be used to explore clinical trial data and build applications for improved trial design, analyze therapeutic risks and patient safety issues, and answer feasibility questions related to site selection and patient recruitment.

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