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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Feedforward Deep Learning Optimizer-based RNA-Seq Women's cancers Detection with a hybrid Classification Models for Biomarker Discovery

Author 1: Waleed Mahmoud Ead
Author 2: Marwa Abouelkhir Abdelazim
Author 3: Mona Mohamed Nasr

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0131233

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 12, 2022.

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Abstract: Women's cancers, signified by breast adenocarcinoma and non-small-cell lung cancers, are a significant threat to women's health. Across the globe, the leading cause of death for women is a group of tumors referred to as "female-oriented cancers". The most recent researches in the classification of molecular tumors is the analysis of women's cancers using RNA-Seq data for precision cancer diagnoses. Furthermore, discovering the different genes’ patterns behaviors will lead to predict the cancer-specific biomarkers to early diagnosis and detection of cancer-specific in women. An overfit model will be resulted due to the high-dimensional data of RNA-Seq from a small samples of data. In this work, we propose a filter-based selection approach for a deep learning-based classification model. In addition, hybrid classification models have been proposed to be compared with the new modified deep learning model. The Experiments’ analysis showed that the proposed filter-based selection approach for a deep learning-based classification model performed better than the other hybrid models in terms of performance evaluation metrics, with an accuracy of 96.7% for RNA-Seq breast adenocarcinoma data and 95.5% for RNA-Seq non-small-cell lung cancer data.

Keywords: Women's cancers; RNA-Seq; deep learning; molecular tumor; hybrid classification models

Waleed Mahmoud Ead, Marwa Abouelkhir Abdelazim and Mona Mohamed Nasr, “Feedforward Deep Learning Optimizer-based RNA-Seq Women's cancers Detection with a hybrid Classification Models for Biomarker Discovery” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131233

@article{Ead2022,
title = {Feedforward Deep Learning Optimizer-based RNA-Seq Women's cancers Detection with a hybrid Classification Models for Biomarker Discovery},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131233},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131233},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Waleed Mahmoud Ead and Marwa Abouelkhir Abdelazim and Mona Mohamed Nasr}
}


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