Log In/Sign Up Become a Member Submit an Idea
Search

Delivering Data Driven Value

Resource Tag: Delivering Data Driven Value

Lynx: A FAIR-Fueled Reference Data Integration & Look up Service at Roche

Roche, as a leading biopharmaceutical company and member of the Pistoia Alliance, has a diverse and distributed ecosystem of platforms to manage reference data standards used at different parts of the organization. These diverse reference data standards include ontologies and vocabularies to capture specifics of the research environment and also to describe how clinical trial data are collected, tabulated, analyzed, and finally submitted to regulatory authorities. In the context of the EDIS program, Roche has bridged these parts to improve reverse translation from studies into research and also embraced FAIR to emphasize machine-actionability and data-driven processes.

In this webinar, we will present and provide technical details of Lynx, a FAIR-fueled system to enable seamless access across that ecosystem. On the one hand, Lynx exploits machine-readable, FAIR Knowledge Graphs to allow for accessing and combining multiple and disparate reference data systems. On the other hand, Lynx bridges the gap for non-experts with an intuitive and user-friendly way of finding and exploring FAIR data.

Speakers

Dr. Javier D. Fernández is a Senior Information Architect at Roche in Basel, Switzerland.

Ontologies Mapping Resources

This area contains public resources for ontology consumers and providers to support practical application and mapping.

The Ontologies Mapping project was set up to create better mapping tools and services, and to establish best practices for ontology management in the Life Sciences. For our purpose, ontologies can include hierarchical relationships, taxonomies, classifications and/or vocabularies which are becoming increasingly important for support of research and development.

Informed Consent in Clinical Trials – Application of Blockchain Technology

Patient ownership and control of personal data and increased regulatory compliance are key areas of improvement in clinical trials. Blockchain is a form of Distributed Ledger Technology (DLT) that supports trust, immutability of transactions and prevents single point of failure.

Blockchain technology involves the implementation of Decentralized Identifiers (DID), ‘virtual’ wallets, Verifiable Credentials (VC), smart contracts, and a blockchain layer to record transactions between parties during the clinical trial Informed Consent process. Blockchain technology puts patients in control of their identity and their data and has the potential to significantly change how patients are enrolled and participate in clinical trials. Further, blockchain technology puts sponsors in control of the Informed Consent process and documentation and has the potential to increase speed and compliance through ‘anytime’, real-time auditing.

Results of the Ontology Alignment Evaluation Initiative 2020

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2020 campaign offered 12 tracks with 36 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign.

Ontology Matching for the Laboratory Analytics Domain

The Pistoia Alliance Ontologies Mapping Project has covered two domains of interest: phenotype and disease, and laboratory analytics domain. In this paper we focus on the latter, for which alignment sets are not that common, we introduce the system Paxo, and we compare its results against participants of the Ontology Alignment Evaluation Initiative.

QDatE Best Practice Guidelines

These Best Practice Guidelines are created to provide a strategy that derives as much value as possible to
the study collecting the data, the participants who supply the data and any external users who are reusing
the data outside of its original intended use.

QDatE Code of Ethics

This Quality Data Generation and Ethical Use (QDatE) code of ethics is complementary to the Best Practice
Guidelines and will ensure that the sensor-generated data from remote monitoring technologies (SDRM) is collected, stored, governed, used, and reused in a way that utilises the data to the best of its potential.

The FAIR Toolkit

Supports the implementation of the Findable, Accessible, Interoperable and Re-usable (FAIR) guiding principles

Key Challenges In Developing A Data Governance Framework

Data is the key asset in biopharmaceutical research; it is highly valued, but how well is it managed?

Developing a rigorous and robust data governance strategy is the foundation for driving innovation and improving the efficiency and effectiveness of R&D. New technologies, such as AI/ML, currently being utilised in modern R&D including its laboratories are dependent on the machine-ready availability (FAIR) of well-categorised data. Furthermore, security and data integrity is crucial for ensuring regulatory compliance.

The first session in the Pistoia Alliance Data Governance Webinar Series will address some of the key challenges in developing a data governance framework.