Evidence map›Paper›PMID 32617779›Full record

ArticleClinical oral investigations2021

Machine learning in prediction of genetic risk of nonsyndromic oral clefts in the Brazilian population.

Renato Assis Machado, Carolina de Oliveira Silva, Hercílio Martelli-Junior, Lucimara Teixeira das Neves, Ricardo D Coletta

Abstract read
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In one paragraph

Article in Clinical oral investigations, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 12% of its field
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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Updates in Cleft Care.Seminars in plastic surgery · 2023
    Review
  6. Article
  7. Article
  8. Article
  9. Machine Learning in Dentistry: A Scoping Review.Journal of clinical medicine · 2023
    Article
  10. Article
  11. Review
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

5 authors at 3 institutions in 1 country.

Renato Assis MachadoDepartment of Oral Diagnosis, School of Dentistry, University of Campinas, Piracicaba, São Paulo, CEP 13414-018, Brazil.
Carolina de Oliveira SilvaDepartment of Oral Diagnosis, School of Dentistry, University of Campinas, Piracicaba, São Paulo, CEP 13414-018, Brazil.
Hercílio Martelli-JuniorStomatology Clinic, Dental School, State University of Montes Claros, Montes Claros, Minas Gerais, Brazil.
Lucimara Teixeira das NevesPost-Graduation Program in Rehabilitation Sciences, Hospital for Rehabilitation of Craniofacial Anomalies, University of São Paulo, Bauru, São Paulo, Brazil.
Ricardo D ColettaDepartment of Oral Diagnosis, School of Dentistry, University of Campinas, Piracicaba, São Paulo, CEP 13414-018, Brazil. coletta@fop.unicamp.br.ORCID http://orcid.org/0000-0001-5285-3046
Universidade de São Paulo · BRUniversidade Estadual de Campinas (UNICAMP) · BRUniversidade José do Rosário Vellano · BR

Funding

Fundação de Amparo à Pesquisa do Estado de São Paulo 2016/02667-0
6 · The paper itself

Abstract

objectivesGenetic variants in multiple genes and loci have been associated with the risk of nonsyndromic cleft lip with or without cleft palate (NSCL ± P). However, the estimation of risk remains challenge, because most of these variants are population-specific rendering the identification of the underlying genetic risk difficult. Herein we examined the use of machine learning network in previously reported single nucleotide polymorphisms (SNPs) to predict risk of NSCL ± P in the Brazilian population. MATERIALS AND

methodsRandom forest and neural network methods were applied in 72 SNPs in a case-control sample composed by 722 NSCL ± P and 866 controls for discrimination of NSCL ± P risk. SNP-SNP interactions and functional annotation biological processes associated with the identified NSCL ± P risk genes were verified.

resultsSupervised random forest decision trees revealed high scores of importance for the SNPs rs11717284 and rs1875735 in FGF12, rs41268753 in GRHL3, rs2236225 in MTHFD1, rs2274976 in MTHFR, rs2235371 and rs642961 in IRF6, rs17085106 in RHPN2, rs28372960 in TCOF1, rs7078160 in VAX1, rs10762573 and rs2131960 in VCL, and rs227731 in 17q22, with an accuracy of 99% and an error rate of approximately 3% to predict the risk of NSCL ± P. Those same 13 SNPs were considered the most important for the neural network to effectively predict NSCL ± P risk, with an overall accuracy of 94%. Multivariate regression model revealed significant interactions among all SNPs, with an exception of those in FGF12 and MTHFD1. The most significantly biological processes for selected genes were those involved in tissue and epithelium development; neural tube closure; and metabolism of methionine, folate, and homocysteine.

conclusionsOur results provide novel clues for genetic mechanism studies of NSCL ± P and point out for a machine learning model composed by 13 SNPs that is capable of predicting NSCL ± P risk. CLINICAL RELEVANCE: Although validation is necessary, this genetic panel can be useful in the near future to assist in NSCL ± P genetic counseling.

Indexed as

Cleft LipCleft PalateBrazilCase-Control StudiesGenetic Predisposition to DiseaseGenotypeHumansInterferon Regulatory FactorsMachine LearningMethylenetetrahydrofolate Dehydrogenase (NADP)Minor Histocompatibility AntigensPolymorphism, Single NucleotideInterferon Regulatory FactorsIRF6 protein, humanMethylenetetrahydrofolate Dehydrogenase (NADP)Minor Histocompatibility AntigensMTHFD1 protein, humanBrazilian populationGenetic counselingMachine learningNonsyndromic oral cleftSingle nucleotide polymorphism

Identifiers

PMID32617779
OpenAlexW3040566422

What OpenQuestion holds

Textmetadata
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.