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DOI: 10.14569/IJACSA.2022.0130911
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Attention-based Long Short Term Memory Model for DNA Damage Prediction in Mammalian Cells

Author 1: Mohammad A. Alsharaiah
Author 2: Laith H. Baniata
Author 3: Omar Adwan
Author 4: Ahmad Adel Abu-Shareha
Author 5: Mosleh Abu Alhaj
Author 6: Qasem Kharma
Author 7: Abdelrahman Hussein
Author 8: Orieb Abualghanam
Author 9: Nabeel Alassaf
Author 10: Mohammad Baniata

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

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Abstract: The understanding of DNA damage intensity – concentration-level is critical for biological and biomedical research, such as cellular homeostasis, tumor suppression, immunity, and gametogenesis. Therefore, recognizing and quantifying DNA damage intensity levels is a substantial issue, which requires further robust and effective approaches. DNA damage has several intensity levels. These levels of DNA damage in malignant cells and in other unhealthy cells are significant in the assessment of lesion stages located in normal cells. There is a need to get more insight from the available biological data to predict, explore and classify DNA damage intensity levels. Herein, the development process relied on the available biological dataset related to DNA damage signaling pathways, which plays a crucial role in DNA damage in the mammalian cell system. The biological dataset that was used in the proposed model consists of 15000 records intensity – concentration-level for a set of five proteins which regulate DNA damage. This research paper proposes an innovative deep learning model, which consists of an attention-based long short term-memory (AT-LSTM) model for DNA damage multi class predictions. The proposed model splits the prediction procedure into dual stages. For the first stage, we adopt the related feature sequences which are inserted as input to the LSTM neural network. In the next stage, the attention feature is applied efficiently to adopt the related feature sequences which are inserted as input to the softmax layer for prediction in the following frame. Our developed framework not only solves the long-term dependence problem of prediction effectively, but also enhances the interpretability of the prediction methods that was established on the neural network. We conducted a novel proposed model on big and complex biological datasets to perform prediction and multi classification tasks. Indeed, the (AT-LSTM) model has the ability to predict and classify the DNA damage in several classes: No-Damage, Low-damage, Medium-damage, High-damage, and Excess-damage. The experimental results show that our framework for DNA damage intensity level can be considered as state of the art for the biological DNA damage prediction domain.

Keywords: Mammalian cell; deep learning techniques; attention; LSTM; classification; DNA damage

Mohammad A. Alsharaiah, Laith H. Baniata, Omar Adwan, Ahmad Adel Abu-Shareha, Mosleh Abu Alhaj, Qasem Kharma, Abdelrahman Hussein, Orieb Abualghanam, Nabeel Alassaf and Mohammad Baniata, “Attention-based Long Short Term Memory Model for DNA Damage Prediction in Mammalian Cells” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130911

@article{Alsharaiah2022,
title = {Attention-based Long Short Term Memory Model for DNA Damage Prediction in Mammalian Cells},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130911},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130911},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Mohammad A. Alsharaiah and Laith H. Baniata and Omar Adwan and Ahmad Adel Abu-Shareha and Mosleh Abu Alhaj and Qasem Kharma and Abdelrahman Hussein and Orieb Abualghanam and Nabeel Alassaf and Mohammad Baniata}
}



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.

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