Evidence map›Paper›PMID 40225702›Full record

ReviewBone reports2025

Can AI reveal the next generation of high-impact bone genomics targets?

Casey S Greene, Christopher R Gignoux, Marc Subirana-Granés, Milton Pividori, Stephanie C Hicks, Cheryl L Ackert-Bicknell

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Bone reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Casey S GreeneDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
Christopher R GignouxDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
Marc Subirana-GranésDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
Milton PividoriDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
Stephanie C HicksDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Cheryl L Ackert-BicknellDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.

Funding

Novel computational strategies to deconvolute co-occurring conditions in Down syndromeR01HD109765 · NICHD · UNIVERSITY OF COLORADO DENVER · PI James Christopher Costello, Casey S Greene · 2022 to 2026
$4.1M
Genomic Approaches to Population Health in Multi-Ethnic Hospital SystemsR01HG011345 · NHGRI · UNIVERSITY OF COLORADO DENVER · PI ARBOLEDA, VALERIE A, GIGNOUX, CHRISTOPHER R · 2020 to 2023
$3.1M
Computational approaches to characterize heterogeneity and improve risk stratification in complex disease phenotypesR00HG011898 · NHGRI · UNIVERSITY OF COLORADO DENVER · PI PIVIDORI, MILTON · 2023 to 2025
$747k
NHGRI NIH HHS R00 HG011898NHGRI NIH HHS R01 HG011345NICHD NIH HHS R01 HD109765
6 · The paper itself

Abstract

Genetic studies have revealed hundreds of loci associated with bone-related phenotypes, including bone mineral density (BMD) and fracture risk. However, translating discovered loci into effective new therapies remains challenging. We review success stories including PCSK9-related drugs in cardiovascular disease and evidence supporting the use of human genetics to guide drug discovery, while highlighting advances in artificial intelligence and machine learning with the potential to improve target discovery in skeletal biology. These strategies are poised to improve how we integrate diverse data types, from genetic and electronic health records data to single-cell profiles and knowledge graphs. Such emerging computational methods can position bone genomics for a future of more precise, effective treatments, ultimately improving the outcomes for patients with common and rare skeletal disorders.

Indexed as

Artificial intelligenceGeneticsKnowledge graphMachine learningSystems biologyTarget discovery

Identifiers

PMID40225702
PMCPMC11986539

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.