2021 6th International Conference for Convergence in Technology (I2CT) 2021
DOI: 10.1109/i2ct51068.2021.9417985
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Deep Learning Techniques in Cancer Prediction Using Genomic Profiles

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Cited by 7 publications
(2 citation statements)
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“…However, over the last decade, deep learning (DL) approaches have surged in popularity. These DL methods excel in predicting the functionality and structure of genomic elements, including promoters, enhancers, and specific gene sequences, offering enhanced insights into genetic mechanisms [12,13]. In the realm of gene expression analysis, feature engineering stands as a crucial computational technique, particularly given the challenge posed by the vast dimensionality of data juxtaposed with a limited sample size.…”
Section: Introductionmentioning
confidence: 99%
“…However, over the last decade, deep learning (DL) approaches have surged in popularity. These DL methods excel in predicting the functionality and structure of genomic elements, including promoters, enhancers, and specific gene sequences, offering enhanced insights into genetic mechanisms [12,13]. In the realm of gene expression analysis, feature engineering stands as a crucial computational technique, particularly given the challenge posed by the vast dimensionality of data juxtaposed with a limited sample size.…”
Section: Introductionmentioning
confidence: 99%
“…Machine learning (ML)-based approaches have been frequently used to obtain insights related to how variations in genes and regulatory regions result in phenotypic changes, such as traits, wellness, and health [10,11]. Whereas early computational methods for gene expression analysis typically relied on conventional ML approaches, such as Decision Trees and Support Vector Machines, in the past ten years, deep learning (DL)-based methods for forecasting the structure and function of genomic components-like promoters, enhancers, or gene sequence levels-have grown in prominence [12,13].…”
Section: Introductionmentioning
confidence: 99%