iEnhancer-Deep: A Computational Predictor for Enhancer Sites and Their Strength Using Deep Learning
Enhancers are short motifs that contain high position variability and free scattering. Identifying these non-coding DNA fragments and their strength is vital because they play an important role in the control of gene regulation. Enhancer identification is more complicated than other genetic factors...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/4/2120 |
| _version_ | 1797483020303728640 |
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| author | Haider Kamran Muhammad Tahir Hilal Tayara Kil To Chong |
| author_facet | Haider Kamran Muhammad Tahir Hilal Tayara Kil To Chong |
| author_sort | Haider Kamran |
| collection | DOAJ |
| description | Enhancers are short motifs that contain high position variability and free scattering. Identifying these non-coding DNA fragments and their strength is vital because they play an important role in the control of gene regulation. Enhancer identification is more complicated than other genetic factors due to free scattering and their very high amount of locational variation. To classify this biological difficulty, several computational tools in bioinformatics have been created over the last few years as current learning models are still lacking. To overcome these limitations, we introduce iEnhancer-Deep, a deep learning-based framework that uses One-Hot Encoding and a convolutional neural network for model construction, primarily for the identification of enhancers and secondarily for the classification of their strength. Parallels between the iEnhancer-Deep and existing state-of-the-art methodologies were drawn to evaluate the performance of the proposed model. Furthermore, a cross-species test was carried out to assess the generalizability of the proposed model. In general, the results show that the proposed model produced comparable results with the state-of-the-art models. |
| first_indexed | 2024-03-09T22:40:54Z |
| format | Article |
| id | doaj.art-b2a1aa064d344113a555d8723dec51c5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:40:54Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b2a1aa064d344113a555d8723dec51c52023-11-23T18:39:37ZengMDPI AGApplied Sciences2076-34172022-02-01124212010.3390/app12042120iEnhancer-Deep: A Computational Predictor for Enhancer Sites and Their Strength Using Deep LearningHaider Kamran0Muhammad Tahir1Hilal Tayara2Kil To Chong3Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Computer Science, Abdul Wali Khan University, Mardan 23200, PakistanSchool of International Engineering and Science, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaEnhancers are short motifs that contain high position variability and free scattering. Identifying these non-coding DNA fragments and their strength is vital because they play an important role in the control of gene regulation. Enhancer identification is more complicated than other genetic factors due to free scattering and their very high amount of locational variation. To classify this biological difficulty, several computational tools in bioinformatics have been created over the last few years as current learning models are still lacking. To overcome these limitations, we introduce iEnhancer-Deep, a deep learning-based framework that uses One-Hot Encoding and a convolutional neural network for model construction, primarily for the identification of enhancers and secondarily for the classification of their strength. Parallels between the iEnhancer-Deep and existing state-of-the-art methodologies were drawn to evaluate the performance of the proposed model. Furthermore, a cross-species test was carried out to assess the generalizability of the proposed model. In general, the results show that the proposed model produced comparable results with the state-of-the-art models.https://www.mdpi.com/2076-3417/12/4/2120enhancer identificationclassificationone-hot encodingdeep learningconvolutional neural network |
| spellingShingle | Haider Kamran Muhammad Tahir Hilal Tayara Kil To Chong iEnhancer-Deep: A Computational Predictor for Enhancer Sites and Their Strength Using Deep Learning Applied Sciences enhancer identification classification one-hot encoding deep learning convolutional neural network |
| title | iEnhancer-Deep: A Computational Predictor for Enhancer Sites and Their Strength Using Deep Learning |
| title_full | iEnhancer-Deep: A Computational Predictor for Enhancer Sites and Their Strength Using Deep Learning |
| title_fullStr | iEnhancer-Deep: A Computational Predictor for Enhancer Sites and Their Strength Using Deep Learning |
| title_full_unstemmed | iEnhancer-Deep: A Computational Predictor for Enhancer Sites and Their Strength Using Deep Learning |
| title_short | iEnhancer-Deep: A Computational Predictor for Enhancer Sites and Their Strength Using Deep Learning |
| title_sort | ienhancer deep a computational predictor for enhancer sites and their strength using deep learning |
| topic | enhancer identification classification one-hot encoding deep learning convolutional neural network |
| url | https://www.mdpi.com/2076-3417/12/4/2120 |
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