AI3SD Video: Data Generation, Data Standards and Metadata Capture in Drug Discovery
AI3SD Video: Data Generation, Data Standards and Metadata Capture in Drug Discovery
Biomedical research and drug discovery are based on a continuous cycle of scientific findings being made, refined, and translated into new treatments. However, over recent years it has become clear that only a fraction of all published research findings are actually reproducible, causing waste and delays in our efforts to bring new drugs to patients. The answer is changing the way we generate and capture data, including experimental metadata. Especially in light of the increasing role of Artificial Intelligence in drug discovery, it is critical to rethink the way we approach data generation as the most important input for AI-driven drug discovery. The talk will address these recent advances in data and metadata capture based on fully automated experimentation and novel data standards.
AI3SD Event, Data Quality, Data Science, Data Sharing, Datasets, Digitisation, Ontologies, RDF, Reproducible Research, Research, Research Data Management, Responsible Research, Semantic Web, Semantics, Training
Immanuel-Bittner, Martin
7b20b24b-8df1-43d2-b916-b8ae2baef1d6
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
Knight, Nicola
fbc21e18-095e-4c1a-a4bf-6277debf5c4b
5 November 2020
Immanuel-Bittner, Martin
7b20b24b-8df1-43d2-b916-b8ae2baef1d6
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
Knight, Nicola
fbc21e18-095e-4c1a-a4bf-6277debf5c4b
Immanuel-Bittner, Martin
(2020)
AI3SD Video: Data Generation, Data Standards and Metadata Capture in Drug Discovery.
Kanza, Samantha, Frey, Jeremy G., Hooper, Victoria and Knight, Nicola
(eds.)
AI3SD, PSDS & Patterns Failed it to Nailed it: Getting Data Sharing Right Seminar Series 2020, Online, Southampton, United Kingdom.
22 Oct - 03 Dec 2020.
(doi:10.5258/SOTON/P0064).
Record type:
Conference or Workshop Item
(Other)
Abstract
Biomedical research and drug discovery are based on a continuous cycle of scientific findings being made, refined, and translated into new treatments. However, over recent years it has become clear that only a fraction of all published research findings are actually reproducible, causing waste and delays in our efforts to bring new drugs to patients. The answer is changing the way we generate and capture data, including experimental metadata. Especially in light of the increasing role of Artificial Intelligence in drug discovery, it is critical to rethink the way we approach data generation as the most important input for AI-driven drug discovery. The talk will address these recent advances in data and metadata capture based on fully automated experimentation and novel data standards.
Video
FI2NI-Event2-Talk3-Martin-ImmanuelBittner-051120
- Version of Record
More information
Published date: 5 November 2020
Additional Information:
MD DPhil is the Chief Executive Officer of Arctoris, the world’s first fully automated drug discovery platform that he cofounded in 2016. He graduated as a medical doctor from the University of Freiburg in Germany, followed by his DPhil in Oncology as a Rhodes scholar at the University of Oxford. Martin has extensive research experience covering both clinical trials and preclinical drug discovery and is an active member of several leading cancer research organisations, including EACR, AACR, and ESTRO. In recognition of his research achievements, he was elected a member of the Young Academy of the German National Academy of Sciences in 2018.
Venue - Dates:
AI3SD, PSDS & Patterns Failed it to Nailed it: Getting Data Sharing Right Seminar Series 2020, Online, Southampton, United Kingdom, 2020-10-22 - 2020-12-03
Keywords:
AI3SD Event, Data Quality, Data Science, Data Sharing, Datasets, Digitisation, Ontologies, RDF, Reproducible Research, Research, Research Data Management, Responsible Research, Semantic Web, Semantics, Training
Identifiers
Local EPrints ID: 447526
URI: http://eprints.soton.ac.uk/id/eprint/447526
PURE UUID: b0bc04e9-8d9f-42cd-a07a-f7c45596925c
Catalogue record
Date deposited: 15 Mar 2021 17:31
Last modified: 17 Mar 2024 03:57
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Contributors
Author:
Martin Immanuel-Bittner
Editor:
Victoria Hooper
Editor:
Nicola Knight
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