Editorial: Artificial intelligence for data discovery and reuse in endocrinology and metabolism

Claudio Angione; Huajin Wang; Noël Burtt

doi:10.3389/fendo.2023.1180254

Editorial: Artificial intelligence for data discovery and reuse in endocrinology and metabolism

Front Endocrinol (Lausanne). 2023 May 5:14:1180254. doi: 10.3389/fendo.2023.1180254. eCollection 2023.

Authors

Claudio Angione^{1

2

3}, Huajin Wang⁴, Noël Burtt⁵

Affiliations

¹ School of Computing, Engineering and Digital Technologies, Teesside University, North Yorkshire, United Kingdom.
² Centre for Digital Innovation, Teesside University, North Yorkshire, United Kingdom.
³ National Horizons Centre, Teesside University, North Yorkshire, United Kingdom.
⁴ Open Science & Data Collaborations, Carnegie Mellon University, Pittsburgh, PA, United States.
⁵ Medical and Population Genetics Program and Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, United States.

No abstract available

Keywords: machine learning; mechanism & characterization; multi-modal; multi-omic integration; systems biology.

Publication types

Editorial
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Artificial Intelligence*
Endocrinology*
Machine Learning

Grants and funding

CA would like to acknowledge The Alan Turing Institute for a Network Development Award, grant TNDC2-100022, and Turing Network Funding, grant D-ELA-013.