Trial reportJCI insight2018
Transcriptomics and machine learning predict diagnosis and severity of growth hormone deficiency.
Trial report in JCI insight, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 13 papers.
What it found
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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.
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.
A Phase IV Open-label Study of Predictive Markers in Growth Hormone Deficient and Turner Syndrome Pre-pubertal Children Treated With SAIZEN®
Observational Long-term Follow-up of the Phase IV Open-label Trial of Predictive Markers in GHD and TS Pre-pubertal Children Treated With Saizen
Who cites it
13 citing papers in PubMed, 27 citations in OpenAlex.
- Gene Expression Signatures Predict First-Year Response to Somapacitan Treatment in Children With Growth Hormone Deficiency.The Journal of clinical endocrinology and metabolism · 2024Trial
- Biologically informed dual deep learning for skeletal maturity prediction in pediatrics.Frontiers in radiology · 2026Article
- a1-antitrypsin, a new biomarker of polycystic ovary syndrome by changing its expression and rhythm.Journal of ovarian research · 2025Article
- Machine Learning-Based Classification of Transcriptome Signatures of Non-Ulcerative Bladder Pain Syndrome.International journal of molecular sciences · 2024Article
- Machine Learning-based Classification of transcriptome Signatures of non-ulcerative Bladder Pain Syndrome.bioRxiv : the preprint server for biology · 2024Article
- Molecular mechanisms of human overgrowth and use ofFrontiers in genetics · 2024Review
- Diagnosis of childhood and adolescent growth hormone deficiency using transcriptomic data.Frontiers in endocrinology · 2023Article
- Nasal DNA methylation at three CpG sites predicts childhood allergic disease.Nature communications · 2022Article
- Scalable transcriptomics analysis with Dask: applications in data science and machine learning.BMC bioinformatics · 2022Review
- A Radiomics-Based Model with the Potential to Differentiate Growth Hormone Deficiency and Idiopathic Short Stature on Sella MRI.Yonsei medical journal · 2022Article
- Development of a Predictive Enrichment Marker for the Oral GH Secretagogue LUM-201 in Pediatric Growth Hormone Deficiency.Journal of the Endocrine Society · 2021Article
- Pharmacogenomics applied to recombinant human growth hormone responses in children with short stature.Reviews in endocrine & metabolic disorders · 2021Review
- A systematic review on machine learning in sellar region diseases: quality and reporting items.Endocrine connections · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe effect of gene expression data on diagnosis remains limited. Here, we show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and random forest analysis.
methodsPrepubertal treatment-naive children with GHD (n = 98) were enrolled from the PREDICT study, and controls (n = 26) were acquired from online data sets. Whole blood gene expression was correlated with peak growth hormone (GH) using rank regression and a random forest algorithm tested for prediction of the presence of GHD and in classification of GHD as severe (peak GH <4 μg/l) and nonsevere (peak ≥4 μg/l). Performance was assessed using area under the receiver operating characteristic curve (AUC-ROC).
resultsRank regression identified 347 probe sets in which gene expression correlated with peak GH concentrations (r = ± 0.28, P < 0.01). These 347 probe sets yielded an AUC-ROC of 0.95 for prediction of GHD status versus controls and an AUC-ROC of 0.93 for prediction of GHD severity.
conclusionThis study demonstrates highly accurate diagnosis and disease classification for GHD using a combination of transcriptomics and random forest analysis.
trial registrationNCT00256126 and NCT00699855.
fundingMerck and the National Institute for Health Research (CL-2012-06-005).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.