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Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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An Optimal Stacked ResNet-BiLSTM-Based Accurate Detection and Classification of Genetic Disorders.

K Nandhini¹, G Tamilpavai²

¹Department of Computer Science and Engineering, Anna University, Chennai, India.

Neural Processing Letters

|June 26, 2023

Summary

This study introduces a novel AI model, the Elephant Herd Optimization-Whale Optimization Algorithm (EHO-WOA) optimized Stacked ResNet-Bidirectional Long Term Short Memory (ResNet-BiLSTM), for accurately detecting genetic disorders from DNA data.

Keywords:

Angelman syndrome Elephant herd optimization Genetic disorder Prader-Willi syndrome Stacked ResNet-bidirectional long short term memory model Whale optimization algorithm

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Area of Science:

Genetics
Bioinformatics
Artificial Intelligence

Background:

Genetic disorders arise from alterations in deoxyribonucleic acid (DNA), potentially causing abnormal gene function.
Accurate diagnosis of genetic abnormalities, including chromosomal, complex, and single-gene disorders, is crucial.
Existing diagnostic methods may require enhancement for precision and scope.

Purpose of the Study:

To develop and validate an advanced computational model for the early and accurate detection of genetic disorders.
To leverage hybrid optimization algorithms with deep learning architectures for improved diagnostic performance.
To identify a range of specific genetic conditions, including rare syndromes.

Main Methods:

A hybrid Elephant Herd Optimization-Whale Optimization Algorithm (EHO-WOA) was employed to optimize a Stacked ResNet-Bidirectional Long Term Short Memory (ResNet-BiLSTM) neural network.
The ResNet-BiLSTM model was trained using genotype and gene expression phenotype data.
The model's performance was evaluated based on key metrics such as accuracy, recall, specificity, precision, and F1-score.

Main Results:

The proposed EHO-WOA optimized ResNet-BiLSTM model demonstrated high accuracy in predicting various genetic disorders.
The model successfully identified rare genetic disorders, including Angelman Syndrome, Rett Syndrome, and Prader-Willi Syndrome.
Performance metrics including accuracy, recall, specificity, precision, and F1-score were significantly enhanced.

Conclusions:

The developed EHO-WOA optimized ResNet-BiLSTM model offers a powerful and accurate tool for diagnosing a wide spectrum of genetic disorders.
This approach shows significant promise for the early detection of conditions like Prader-Willi syndrome, Marfan syndrome, Early Onset Morbid Obesity, Rett syndrome, and Angelman syndrome.
The integration of hybrid optimization with deep learning provides a robust framework for advancing genetic disorder diagnostics.