Deep-piRNA: bi-layered prediction model for PIWI-interacting RNA using discriminative features
Piwi-interacting Ribonucleic acids (piRNAs) molecule is a well-known subclass of small non-coding RNA molecules that are mainly responsible for maintaining genome integrity, regulating gene expression, and germline stem cell maintenance by suppressing transposon elements. The piRNAs molecule can be...
| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Tech Science Press
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/100876/ |
| _version_ | 1848863436765659136 |
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| author | Salman Khan Mukhtaj Khan Nadeem Iqbal Abd Rahman, Mohd Amiruddin Abdul Karim, Muhammad Khalis |
| author_facet | Salman Khan Mukhtaj Khan Nadeem Iqbal Abd Rahman, Mohd Amiruddin Abdul Karim, Muhammad Khalis |
| author_sort | Salman Khan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Piwi-interacting Ribonucleic acids (piRNAs) molecule is a well-known subclass of small non-coding RNA molecules that are mainly responsible for maintaining genome integrity, regulating gene expression, and germline stem cell maintenance by suppressing transposon elements. The piRNAs molecule can be used for the diagnosis of multiple tumor types and drug development. Due to the vital roles of the piRNA in computational biology, the identification of piRNAs has become an important area of research in computational biology. This paper proposes a two-layer predictor to improve the prediction of piRNAs and their function using deep learning methods. The proposed model applies various feature extraction methods to consider both structure information and physicochemical properties of the biological sequences during the feature extraction process. The outcome of the proposed model is extensively evaluated using the k-fold cross-validation method. The evaluation result shows that the proposed predictor performed better than the existing models with accuracy improvement of 7.59% and 2.81% at layer I and layer II respectively. It is anticipated that the proposed model could be a beneficial tool for cancer diagnosis and precision medicine. |
| first_indexed | 2025-11-15T13:32:54Z |
| format | Article |
| id | upm-100876 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:32:54Z |
| publishDate | 2022 |
| publisher | Tech Science Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1008762023-07-26T02:55:46Z http://psasir.upm.edu.my/id/eprint/100876/ Deep-piRNA: bi-layered prediction model for PIWI-interacting RNA using discriminative features Salman Khan Mukhtaj Khan Nadeem Iqbal Abd Rahman, Mohd Amiruddin Abdul Karim, Muhammad Khalis Piwi-interacting Ribonucleic acids (piRNAs) molecule is a well-known subclass of small non-coding RNA molecules that are mainly responsible for maintaining genome integrity, regulating gene expression, and germline stem cell maintenance by suppressing transposon elements. The piRNAs molecule can be used for the diagnosis of multiple tumor types and drug development. Due to the vital roles of the piRNA in computational biology, the identification of piRNAs has become an important area of research in computational biology. This paper proposes a two-layer predictor to improve the prediction of piRNAs and their function using deep learning methods. The proposed model applies various feature extraction methods to consider both structure information and physicochemical properties of the biological sequences during the feature extraction process. The outcome of the proposed model is extensively evaluated using the k-fold cross-validation method. The evaluation result shows that the proposed predictor performed better than the existing models with accuracy improvement of 7.59% and 2.81% at layer I and layer II respectively. It is anticipated that the proposed model could be a beneficial tool for cancer diagnosis and precision medicine. Tech Science Press 2022-03-29 Article PeerReviewed Salman Khan and Mukhtaj Khan and Nadeem Iqbal and Abd Rahman, Mohd Amiruddin and Abdul Karim, Muhammad Khalis (2022) Deep-piRNA: bi-layered prediction model for PIWI-interacting RNA using discriminative features. Computers, Materials & Continua, 72 (2). 2243 - 2258. ISSN 1546-2218; ESSN: 1546-2226 https://techscience.com/cmc/v72n2/47151 10.32604/cmc.2022.022901 |
| spellingShingle | Salman Khan Mukhtaj Khan Nadeem Iqbal Abd Rahman, Mohd Amiruddin Abdul Karim, Muhammad Khalis Deep-piRNA: bi-layered prediction model for PIWI-interacting RNA using discriminative features |
| title | Deep-piRNA: bi-layered prediction model for PIWI-interacting RNA using discriminative features |
| title_full | Deep-piRNA: bi-layered prediction model for PIWI-interacting RNA using discriminative features |
| title_fullStr | Deep-piRNA: bi-layered prediction model for PIWI-interacting RNA using discriminative features |
| title_full_unstemmed | Deep-piRNA: bi-layered prediction model for PIWI-interacting RNA using discriminative features |
| title_short | Deep-piRNA: bi-layered prediction model for PIWI-interacting RNA using discriminative features |
| title_sort | deep-pirna: bi-layered prediction model for piwi-interacting rna using discriminative features |
| url | http://psasir.upm.edu.my/id/eprint/100876/ http://psasir.upm.edu.my/id/eprint/100876/ http://psasir.upm.edu.my/id/eprint/100876/ |