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RETRACTED ARTICLE: Analyzing gene polymorphism and metal folic acid interactions in neural tube defects using optimized deep recurrent neural networks

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This article was retracted on 28 August 2023

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Abstract

Recently, increasing importance has been given to neural tube defects (NTDs), considered a congenital disability that affects the brain and the spinal cord. NTD occurs because of genetic information passed from parents to children via genes that affect the brain region’s shape or function. Presently, minimum folate carrier (MFC A80G) gene polymorphism and maternal folic acid interactions are associated with NTD. Therefore, we designed and developed a grey wolf optimizer-assisted deep recurrent neural network to predict the association between gene polymorphism and folic acid interaction in NTD. The information of different control families and nuclear family information was collected to examine the process of polymorphism. Moreover, the homozygous mutant type (GG) genotype mothers and folic acid consumption levels were continuously analyzed to identify the link with and risk of NTD. The simulation analysis was performed to evaluate the statistical results in comparison with those obtained using conventional methods. Given the importance of interactions and associations, this research discusses the optimized deep recurrent neural network-based polymorphism process to identify the NTD risk factors with a lower error ratio of 0.015%. We found that MFC-GG genotype polymorphism and folic acid consumptions are essential and effective in lowering the prevalence of NTD among offspring with maximum accuracy rate of 99.5%.

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Abbreviations

x :

input value in gene-environment

h t :

hidden layer output

U h :

previous hidden layer value

σ h :

activation function

W h :

weight value

b h :

bias value

y t :

network output

α :

first grey wolves in the regime

β :

second level of authority

δ, ω :

dominates wolves

t :

current iteration

\( \overrightarrow{A} \), \( \overrightarrow{c} \) :

coefficient vectors

\( \overrightarrow{x_p}(t) \) :

prey position vector

\( \overrightarrow{x} \) :

grey wolf position

\( \overrightarrow{r_1} \), \( \overrightarrow{r_2\ } \) :

random vectors

\( \overrightarrow{D} \) :

wolf’s behavior

NTD:

neural tube defects

MTHFR:

methylenetetra hydrofolate reductase gene

MFC:

minimum folate carrier

PHC:

perinatal health care

PCR:

polymerase chain reaction

SNP:

single nucleotide polymorphism

ODRNN:

optimized deep recurrent neural network

ANN:

artificial neural networks

MLP:

multilayer perceptron

BPNN:

backpropagation neural networks

RBFN:

radial basis function networks

CDC:

Center for Disease Control

ECG:

electrocardiography

DNA:

deoxyribonucleic acid

OR:

odds ratios

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Acknowledgements

The authors would like to extend their gratitude to King Saud University (Riyadh, Saudi Arabia) for funding this research through Researchers Supporting Project number (RSP-2020/241).

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Authors and Affiliations

  1. Biomedical Engineering Department, Faculty of Engineering, Helwan University, Cairo, Egypt

    Ibrahim Mustafa

  2. Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia

    Aldosary Saad

  3. Department of Biochemistry, College of Science, King Saud University, PO Box 22452, Riyadh, 11451, Saudi Arabia

    Mohamed H. Mahmoud & Salman Alamery

  4. Biomedical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt

    Nourelhoda M. Mahmoud

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  1. Ibrahim Mustafa
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  2. Aldosary Saad
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Correspondence to Nourelhoda M. Mahmoud.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00779-023-01750-z

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Mustafa, I., Saad, A., Mahmoud, M.H. et al. RETRACTED ARTICLE: Analyzing gene polymorphism and metal folic acid interactions in neural tube defects using optimized deep recurrent neural networks. Pers Ubiquit Comput 27, 861–873 (2023). https://doi.org/10.1007/s00779-021-01538-z

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