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

Claudio Angione, Huajin Wang, Noël Burtt

Research output: Contribution to journal › Editorial › peer-review

Abstract

As biomedical research has embraced the era of big data, massive amounts of complex multi-omic data are being generated. While there is huge potential in using the rich body of data to make new discoveries, many challenges exist in the dissemination, discovery, and reuse of these data. Artificial Intelligence (AI) and machine learning (ML) technologies have been paramount towards fully extracting value from rich and complex datasets to drive scientific discoveries and clinical decision-making. However, the adoption of AI and ML in endocrinology and metabolic diseases is lagging behind, compared to fields such as cancer genomics

Original language	English
Article number	1180254
Journal	Frontiers in Endocrinology
Volume	14
DOIs	https://doi.org/10.3389/fendo.2023.1180254
Publication status	Published - 5 May 2023

Bibliographical note

Funding Information:
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.

Access to Document

10.3389/fendo.2023.1180254

Cite this

@article{2da9a010c7d5453baaead987e20b4a8f,

title = "Editorial: Artificial intelligence for data discovery and reuse in endocrinology and metabolism",

abstract = "As biomedical research has embraced the era of big data, massive amounts of complex multi-omic data are being generated. While there is huge potential in using the rich body of data to make new discoveries, many challenges exist in the dissemination, discovery, and reuse of these data. Artificial Intelligence (AI) and machine learning (ML) technologies have been paramount towards fully extracting value from rich and complex datasets to drive scientific discoveries and clinical decision-making. However, the adoption of AI and ML in endocrinology and metabolic diseases is lagging behind, compared to fields such as cancer genomics",

author = "Claudio Angione and Huajin Wang and No{\"e}l Burtt",

note = "Funding Information: 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.",

year = "2023",

month = may,

day = "5",

doi = "10.3389/fendo.2023.1180254",

language = "English",

volume = "14",

journal = "Frontiers in Endocrinology",

issn = "1664-2392",

publisher = "Frontiers Media",

}

TY - JOUR

T1 - Editorial

T2 - Artificial intelligence for data discovery and reuse in endocrinology and metabolism

AU - Angione, Claudio

AU - Wang, Huajin

AU - Burtt, Noël

N1 - Funding Information: 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.

PY - 2023/5/5

Y1 - 2023/5/5

N2 - As biomedical research has embraced the era of big data, massive amounts of complex multi-omic data are being generated. While there is huge potential in using the rich body of data to make new discoveries, many challenges exist in the dissemination, discovery, and reuse of these data. Artificial Intelligence (AI) and machine learning (ML) technologies have been paramount towards fully extracting value from rich and complex datasets to drive scientific discoveries and clinical decision-making. However, the adoption of AI and ML in endocrinology and metabolic diseases is lagging behind, compared to fields such as cancer genomics

AB - As biomedical research has embraced the era of big data, massive amounts of complex multi-omic data are being generated. While there is huge potential in using the rich body of data to make new discoveries, many challenges exist in the dissemination, discovery, and reuse of these data. Artificial Intelligence (AI) and machine learning (ML) technologies have been paramount towards fully extracting value from rich and complex datasets to drive scientific discoveries and clinical decision-making. However, the adoption of AI and ML in endocrinology and metabolic diseases is lagging behind, compared to fields such as cancer genomics

U2 - 10.3389/fendo.2023.1180254

DO - 10.3389/fendo.2023.1180254

M3 - Editorial

C2 - 37214239

SN - 1664-2392

VL - 14

JO - Frontiers in Endocrinology

JF - Frontiers in Endocrinology

M1 - 1180254

ER -

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

Abstract

Bibliographical note

Access to Document

Fingerprint

Cite this