Genetic Disorders Prediction From Multi-Omics Using Deep Learning Approaches: A Comprehensive Review
DOI:
https://doi.org/10.29284/jxce6d68Keywords:
Genetic disorders, multi-omics, deep learning, convolutional neural networks, recurrent neural networks, transfer learning, interpretability, genetic disease prediction.Abstract
Genetic disorders pose a substantial challenge to public health due to their many facets and capacity to affect people throughout their lives. The emergence of high-throughput genomic technologies, and the growing availability of multi-omics datasets make the implementation of sophisticated techniques, including deep learning, possible for predicting and identifying genetic disorders at an early stage. This review paper focuses on sophisticated deep learning techniques and their application in the prediction of genetic disorders from multi-omics data. The review encompasses various deep learning designs, including convolutional, recurrent, and generative adversarial networks, as well as their use in genomics and related fields. Moreover, the review analyses how multi-omics data is integrated to improve the accuracy of the predictive models using transfer learning, attention, and graph neural networks. This paper also describes successful cases of utilizing deep learning to predict various genetic disorders such as neurodegenerative disease, cancer predisposition syndromes, cardiovascular disorders, and several rare genetic conditions. Another contributed topic is the results of applying deep learning to general problems of multi-dimensional data integration, and the comparison of the results to approaches based on traditional machine learning. After discussing these deep learning models for genetic disorder prediction, the review demonstrates the necessity of deep evaluation, concerns of model interpretability, and the use and value of these models for aiding health decision making. The objective of this review is to assist researchers and clinicians wishing to personalize predictions and intervene early for patients with genetic disorders through the utilization of deep learning.
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