Evidence map›Paper›PMID 35906312›Full record

ReviewPediatric research2023

Putting the "mi" in omics: discovering miRNA biomarkers for pediatric precision care.

Chengyin Li, Rhea E Sullivan, Dongxiao Zhu, Steven D Hicks

Abstract readReview
In one paragraph

Review in Pediatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. 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

4 authors.

Chengyin Li *Department of Computer Science, Wayne State University, Detroit, MI, USA.
Rhea E Sullivan *Department of Pediatrics, Penn State College of Medicine, Hershey, PA, USA.
Dongxiao ZhuDepartment of Computer Science, Wayne State University, Detroit, MI, USA.
Steven D HicksDepartment of Pediatrics, Penn State College of Medicine, Hershey, PA, USA. shicks1@pennstatehealth.psu.edu.

Funding

Poly-omic predictors of symptom duration and recovery for adolescent concussionR01NS115942 · NINDS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI HICKS, STEVEN DANIEL · 2021 to 2025
$2.8M
Severity Predictors Integrating salivary Transcriptomics and proteomics with Multi neural network Intelligence in SARS-CoV2 infection in Children (SPITS MISC)R33HD105610 · NICHD · CENTRAL MICHIGAN UNIVERSITY · PI HICKS, STEVEN DANIEL, SETHURAMAN, USHA · 2023 to 2023
$1.4M
Severity Predictors Integrating salivary Transcriptomics and proteomics with Multi neural network Intelligence in SARS-CoV2 infection in Children (SPITS MISC)R61HD105610 · NICHD · CENTRAL MICHIGAN UNIVERSITY · PI HICKS, STEVEN DANIEL, SETHURAMAN, USHA · 2021 to 2022
$1.4M
Understanding the miRNA response to opioid withdrawal and their uses as potential biomarkers for neonatal abstinence syndromeF30DA057094 · NIDA · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI Rhea Elena Sullivan · 2022 to 2026
$213k
NICHD NIH HHS R33 HD105610NICHD NIH HHS R61 HD105610NIDA NIH HHS F30 DA057094NINDS NIH HHS R01 NS115942
6 · The paper itself

Abstract

In the past decade, growing interest in micro-ribonucleic acids (miRNAs) has catapulted these small, non-coding nucleic acids to the forefront of biomarker research. Advances in scientific knowledge have made it clear that miRNAs play a vital role in regulating cellular physiology throughout the human body. Perturbations in miRNA signaling have also been described in a variety of pediatric conditions-from cancer, to renal failure, to traumatic brain injury. Likewise, the number of studies across pediatric disciplines that pair patient miRNA-omics with longitudinal clinical data are growing. Analyses of these voluminous, multivariate data sets require understanding of pediatric phenotypic data, data science, and genomics. Use of machine learning techniques to aid in biomarker detection have helped decipher background noise from biologically meaningful changes in the data. Further, emerging research suggests that miRNAs may have potential as therapeutic targets for pediatric precision care. Here, we review current miRNA biomarkers of pediatric diseases and studies that have combined machine learning techniques, miRNA-omics, and patient health data to identify novel biomarkers and potential therapeutics for pediatric diseases. IMPACT: In the following review article, we summarized how recent developments in microRNA research may be coupled with machine learning techniques to advance pediatric precision care.

Indexed as

MicroRNAsNeoplasmsBiomarkersChildGenomicsHumansMachine LearningBiomarkersMicroRNAs

Identifiers

PMID35906312
PMCPMC9884316

What OpenQuestion holds

Textmetadata
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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 OpenQuestion graph.