Artificial intelligence models for genomics analysis: review article

El-Attar, Noha E. and El-Mashad, Yehia. A. (2024) Artificial intelligence models for genomics analysis: review article. In: Agria Média 2023 : „A magas szintű digitális kompetencia a jövő oktatásának kulcsa”. Eszterházy Károly Katolikus Egyetem Líceum Kiadó, pp. 134-150.

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Abstract

Artificial intelligence (AI) including machine learning (ML), and deep learning (DL) models have become powerful tools for analyzing genomics data in recent years. These models can process large amounts of data and identify complex patterns that may not be apparent through traditional statistical methods. ML and DL models have been used for a wide range of genomics applications, including gene expression analysis, variant detection, and drug discovery. One popular approach for using ML and DL models in genomics is to train these models on large datasets of genomic information. These datasets may include information on gene expression levels, DNA sequences, and epigenetic modifications. By training these models on large datasets, researchers can identify patterns and correlations that may be used to predict disease risk, identify potential drug targets, and develop personalized treatments. Generally, the use of different AI models in genomics has the potential to transform the field by enabling more accurate and personalized medical treatments. As these models continue to evolve and improve, researchers will be able to extract even more information from genomic data and accelerate the pace of discovery in genomics.

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