1
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Ali M, Shah D, Qazi S, Khan IA, Abrar M, Zahir S. An effective deep learning-based approach for splice site identification in gene expression. Sci Prog 2024; 107:368504241266588. [PMID: 39051530 PMCID: PMC11273556 DOI: 10.1177/00368504241266588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
A crucial stage in eukaryote gene expression involves mRNA splicing by a protein assembly known as the spliceosome. This step significantly contributes to generating and properly operating the ultimate gene product. Since non-coding introns disrupt eukaryotic genes, splicing entails the elimination of introns and joining exons to create a functional mRNA molecule. Nevertheless, accurately finding splice sequence sites using various molecular biology techniques and other biological approaches is complex and time-consuming. This paper presents a precise and reliable computer-aided diagnosis (CAD) technique for the rapid and correct identification of splice site sequences. The proposed deep learning-based framework uses long short-term memory (LSTM) to extract distinct patterns from RNA sequences, enabling rapid and accurate point mutation sequence mapping. The proposed network employs one-hot encodings to find sequential patterns that effectively identify splicing sites. A thorough ablation study of traditional machine learning, one-dimensional convolutional neural networks (1D-CNNs), and recurrent neural networks (RNNs) models was conducted. The proposed LSTM network outperformed existing state-of-the-art approaches, improving accuracy by 3% and 2% for the acceptor and donor sites datasets.
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Affiliation(s)
- Mohsin Ali
- Department of Computer Science, Bacha Khan University, Charsadda, KP, Pakistan
| | - Dilawar Shah
- Department of Computer Science, Bacha Khan University, Charsadda, KP, Pakistan
| | - Shahid Qazi
- Department of Computer Science, Bacha Khan University, Charsadda, KP, Pakistan
| | - Izaz Ahmad Khan
- Department of Computer Science, Bacha Khan University, Charsadda, KP, Pakistan
| | - Mohammad Abrar
- Faculty of Computer Science, Arab Open University, Muscat, Oman, Sultanate of Oman
| | - Sana Zahir
- Institute of Computer Sciences and Information Technology, The University of Agriculture Peshawar, Peshawar, KP, Pakistan
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2
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Hu W, Li Y, Wu Y, Guan L, Li M. A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding. iScience 2024; 27:110030. [PMID: 38868182 PMCID: PMC11167433 DOI: 10.1016/j.isci.2024.110030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 04/23/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024] Open
Abstract
Enhancers, genomic DNA elements, regulate neighboring gene expression crucial for biological processes like cell differentiation and stress response. However, current machine learning methods for predicting DNA enhancers often underutilize hidden features in gene sequences, limiting model accuracy. Hence, this article proposes the PDCNN model, a deep learning-based enhancer prediction method. PDCNN extracts statistical nucleotide representations from gene sequences, discerning positional distribution information of nucleotides in modifier-like DNA sequences. With a convolutional neural network structure, PDCNN employs dual convolutional and fully connected layers. The cross-entropy loss function iteratively updates using a gradient descent algorithm, enhancing prediction accuracy. Model parameters are fine-tuned to select optimal combinations for training, achieving over 95% accuracy. Comparative analysis with traditional methods and existing models demonstrates PDCNN's robust feature extraction capability. It outperforms advanced machine learning methods in identifying DNA enhancers, presenting an effective method with broad implications for genomics, biology, and medical research.
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Affiliation(s)
- Wenxing Hu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, Jiangxi, China
| | - Yelin Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, Jiangxi, China
| | - Yan Wu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, Jiangxi, China
| | - Lixin Guan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, Jiangxi, China
| | - Mengshan Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, Jiangxi, China
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3
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Mehmood F, Arshad S, Shoaib M. ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction. Brief Bioinform 2024; 25:bbae030. [PMID: 38385876 PMCID: PMC10885011 DOI: 10.1093/bib/bbae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/30/2023] [Accepted: 01/11/2024] [Indexed: 02/23/2024] Open
Abstract
Enhancers play an important role in the process of gene expression regulation. In DNA sequence abundance or absence of enhancers and irregularities in the strength of enhancers affects gene expression process that leads to the initiation and propagation of diverse types of genetic diseases such as hemophilia, bladder cancer, diabetes and congenital disorders. Enhancer identification and strength prediction through experimental approaches is expensive, time-consuming and error-prone. To accelerate and expedite the research related to enhancers identification and strength prediction, around 19 computational frameworks have been proposed. These frameworks used machine and deep learning methods that take raw DNA sequences and predict enhancer's presence and strength. However, these frameworks still lack in performance and are not useful in real time analysis. This paper presents a novel deep learning framework that uses language modeling strategies for transforming DNA sequences into statistical feature space. It applies transfer learning by training a language model in an unsupervised fashion by predicting a group of nucleotides also known as k-mers based on the context of existing k-mers in a sequence. At the classification stage, it presents a novel classifier that reaps the benefits of two different architectures: convolutional neural network and attention mechanism. The proposed framework is evaluated over the enhancer identification benchmark dataset where it outperforms the existing best-performing framework by 5%, and 9% in terms of accuracy and MCC. Similarly, when evaluated over the enhancer strength prediction benchmark dataset, it outperforms the existing best-performing framework by 4%, and 7% in terms of accuracy and MCC.
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Affiliation(s)
- Faiza Mehmood
- Department of Computer Science, University of Engineering and Technology Lahore, (Faisalabad Campus) Pakistan
| | - Shazia Arshad
- Department of Computer Science, University of Engineering and Technology Lahore, 54890, Pakistan
| | - Muhammad Shoaib
- Department of Computer Science, University of Engineering and Technology Lahore, 54890, Pakistan
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4
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Feng T, Hu T, Liu W, Zhang Y. Enhancer Recognition: A Transformer Encoder-Based Method with WGAN-GP for Data Augmentation. Int J Mol Sci 2023; 24:17548. [PMID: 38139375 PMCID: PMC10743946 DOI: 10.3390/ijms242417548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 11/29/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023] Open
Abstract
Enhancers are located upstream or downstream of key deoxyribonucleic acid (DNA) sequences in genes and can adjust the transcription activity of neighboring genes. Identifying enhancers and determining their functions are important for understanding gene regulatory networks and expression regulatory mechanisms. However, traditional enhancer recognition relies on manual feature engineering, which is time-consuming and labor-intensive, making it difficult to perform large-scale recognition analysis. In addition, if the original dataset is too small, there is a risk of overfitting. In recent years, emerging methods, such as deep learning, have provided new insights for enhancing identification. However, these methods also present certain challenges. Deep learning models typically require a large amount of high-quality data, and data acquisition demands considerable time and resources. To address these challenges, in this paper, we propose a data-augmentation method based on generative adversarial networks to solve the problem of small datasets. Moreover, we used regularization methods such as weight decay to improve the generalizability of the model and alleviate overfitting. The Transformer encoder was used as the main component to capture the complex relationships and dependencies in enhancer sequences. The encoding layer was designed based on the principle of k-mers to preserve more information from the original DNA sequence. Compared with existing methods, the proposed approach made significant progress in enhancing the accuracy and strength of enhancer identification and prediction, demonstrating the effectiveness of the proposed method. This paper provides valuable insights for enhancer analysis and is of great significance for understanding gene regulatory mechanisms and studying disease correlations.
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Affiliation(s)
- Tianyu Feng
- College of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China; (T.F.); (T.H.)
| | - Tao Hu
- College of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China; (T.F.); (T.H.)
| | - Wenyu Liu
- College of Ecology, Lanzhou University, Lanzhou 730000, China;
| | - Yang Zhang
- Supercomputer Center, Lanzhou University, Lanzhou 730000, China
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5
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Phan LT, Oh C, He T, Manavalan B. A comprehensive revisit of the machine-learning tools developed for the identification of enhancers in the human genome. Proteomics 2023; 23:e2200409. [PMID: 37021401 DOI: 10.1002/pmic.202200409] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/18/2023] [Accepted: 03/27/2023] [Indexed: 04/07/2023]
Abstract
Enhancers are non-coding DNA elements that play a crucial role in enhancing the transcription rate of a specific gene in the genome. Experiments for identifying enhancers can be restricted by their conditions and involve complicated, time-consuming, laborious, and costly steps. To overcome these challenges, computational platforms have been developed to complement experimental methods that enable high-throughput identification of enhancers. Over the last few years, the development of various enhancer computational tools has resulted in significant progress in predicting putative enhancers. Thus, researchers are now able to use a variety of strategies to enhance and advance enhancer study. In this review, an overview of machine learning (ML)-based prediction methods for enhancer identification and related databases has been provided. The existing enhancer-prediction methods have also been reviewed regarding their algorithms, feature selection processes, validation techniques, and software utility. In addition, the advantages and drawbacks of these ML approaches and guidelines for developing bioinformatic tools have been highlighted for a more efficient enhancer prediction. This review will serve as a useful resource for experimentalists in selecting the appropriate ML tool for their study, and for bioinformaticians in developing more accurate and advanced ML-based predictors.
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Affiliation(s)
- Le Thi Phan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea
| | - Changmin Oh
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea
| | - Tao He
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea
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6
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Aladhadh S, Almatroodi SA, Habib S, Alabdulatif A, Khattak SU, Islam M. An Efficient Lightweight Hybrid Model with Attention Mechanism for Enhancer Sequence Recognition. Biomolecules 2022; 13:biom13010070. [PMID: 36671456 PMCID: PMC9855522 DOI: 10.3390/biom13010070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/22/2022] [Accepted: 12/26/2022] [Indexed: 12/31/2022] Open
Abstract
Enhancers are sequences with short motifs that exhibit high positional variability and free scattering properties. Identification of these noncoding DNA fragments and their strength are extremely important because they play a key role in controlling gene regulation on a cellular basis. The identification of enhancers is more complex than that of other factors in the genome because they are freely scattered, and their location varies widely. In recent years, bioinformatics tools have enabled significant improvement in identifying this biological difficulty. Cell line-specific screening is not possible using these existing computational methods based solely on DNA sequences. DNA segment chromatin accessibility may provide useful information about its potential function in regulation, thereby identifying regulatory elements based on its chromatin accessibility. In chromatin, the entanglement structure allows positions far apart in the sequence to encounter each other, regardless of their proximity to the gene to be acted upon. Thus, identifying enhancers and assessing their strength is difficult and time-consuming. The goal of our work was to overcome these limitations by presenting a convolutional neural network (CNN) with attention-gated recurrent units (AttGRU) based on Deep Learning. It used a CNN and one-hot coding to build models, primarily to identify enhancers and secondarily to classify their strength. To test the performance of the proposed model, parallels were drawn between enhancer-CNNAttGRU and existing state-of-the-art methods to enable comparisons. The proposed model performed the best for predicting stage one and stage two enhancer sequences, as well as their strengths, in a cross-species analysis, achieving best accuracy values of 87.39% and 84.46%, respectively. Overall, the results showed that the proposed model provided comparable results to state-of-the-art models, highlighting its usefulness.
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Affiliation(s)
- Suliman Aladhadh
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
- Correspondence:
| | - Saleh A. Almatroodi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Shabana Habib
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Abdulatif Alabdulatif
- Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Saeed Ullah Khattak
- Centre of Biotechnology and Microbiology, University of Peshawar, Peshawar 25120, Pakistan
| | - Muhammad Islam
- Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi Arabia
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7
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Huang G, Luo W, Zhang G, Zheng P, Yao Y, Lyu J, Liu Y, Wei DQ. Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition. Biomolecules 2022; 12:biom12070995. [PMID: 35883552 PMCID: PMC9313278 DOI: 10.3390/biom12070995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/03/2022] [Accepted: 07/07/2022] [Indexed: 01/27/2023] Open
Abstract
Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory (Bi-LSTM) and attention-based deep learning method (Enhancer-LSTMAtt) for enhancer recognition. Enhancer-LSTMAtt is an end-to-end deep learning model that consists mainly of deep residual neural network, Bi-LSTM, and feed-forward attention. We extensively compared the Enhancer-LSTMAtt with 19 state-of-the-art methods by 5-fold cross validation, 10-fold cross validation and independent test. Enhancer-LSTMAtt achieved competitive performances, especially in the independent test. We realized Enhancer-LSTMAtt into a user-friendly web application. Enhancer-LSTMAtt is applicable not only to recognizing enhancers, but also to distinguishing strong enhancer from weak enhancers. Enhancer-LSTMAtt is believed to become a promising tool for identifying enhancers.
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Affiliation(s)
- Guohua Huang
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (W.L.); (G.Z.); (P.Z.); (J.L.)
- Correspondence:
| | - Wei Luo
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (W.L.); (G.Z.); (P.Z.); (J.L.)
| | - Guiyang Zhang
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (W.L.); (G.Z.); (P.Z.); (J.L.)
| | - Peijie Zheng
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (W.L.); (G.Z.); (P.Z.); (J.L.)
| | - Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China;
| | - Jianyi Lyu
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (W.L.); (G.Z.); (P.Z.); (J.L.)
| | - Yuewu Liu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410083, China;
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China;
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8
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Geng Q, Yang R, Zhang L. A deep learning framework for enhancer prediction using word embedding and sequence generation. Biophys Chem 2022; 286:106822. [DOI: 10.1016/j.bpc.2022.106822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 04/21/2022] [Accepted: 04/29/2022] [Indexed: 11/28/2022]
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9
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Bhukya R, Kumari A, Amilpur S, Dasari CM. PPred-PCKSM: A multi-layer predictor for identifying promoter and its variants using position based features. Comput Biol Chem 2022; 97:107623. [DOI: 10.1016/j.compbiolchem.2022.107623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 01/02/2022] [Accepted: 01/05/2022] [Indexed: 11/03/2022]
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10
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iEnhancer-GAN: A Deep Learning Framework in Combination with Word Embedding and Sequence Generative Adversarial Net to Identify Enhancers and Their Strength. Int J Mol Sci 2021; 22:ijms22073589. [PMID: 33808317 PMCID: PMC8036415 DOI: 10.3390/ijms22073589] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 03/10/2021] [Accepted: 03/24/2021] [Indexed: 12/13/2022] Open
Abstract
As critical components of DNA, enhancers can efficiently and specifically manipulate the spatial and temporal regulation of gene transcription. Malfunction or dysregulation of enhancers is implicated in a slew of human pathology. Therefore, identifying enhancers and their strength may provide insights into the molecular mechanisms of gene transcription and facilitate the discovery of candidate drug targets. In this paper, a new enhancer and its strength predictor, iEnhancer-GAN, is proposed based on a deep learning framework in combination with the word embedding and sequence generative adversarial net (Seq-GAN). Considering the relatively small training dataset, the Seq-GAN is designed to generate artificial sequences. Given that each functional element in DNA sequences is analogous to a “word” in linguistics, the word segmentation methods are proposed to divide DNA sequences into “words”, and the skip-gram model is employed to transform the “words” into digital vectors. In view of the powerful ability to extract high-level abstraction features, a convolutional neural network (CNN) architecture is constructed to perform the identification tasks, and the word vectors of DNA sequences are vertically concatenated to form the embedding matrices as the input of the CNN. Experimental results demonstrate the effectiveness of the Seq-GAN to expand the training dataset, the possibility of applying word segmentation methods to extract “words” from DNA sequences, the feasibility of implementing the skip-gram model to encode DNA sequences, and the powerful prediction ability of the CNN. Compared with other state-of-the-art methods on the training dataset and independent test dataset, the proposed method achieves a significantly improved overall performance. It is anticipated that the proposed method has a certain promotion effect on enhancer related fields.
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11
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Tan KK, Le NQK, Yeh HY, Chua MCH. Ensemble of Deep Recurrent Neural Networks for Identifying Enhancers via Dinucleotide Physicochemical Properties. Cells 2019; 8:cells8070767. [PMID: 31340596 PMCID: PMC6678823 DOI: 10.3390/cells8070767] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 07/19/2019] [Accepted: 07/21/2019] [Indexed: 12/21/2022] Open
Abstract
Enhancers are short deoxyribonucleic acid fragments that assume an important part in the genetic process of gene expression. Due to their possibly distant location relative to the gene that is acted upon, the identification of enhancers is difficult. There are many published works focused on identifying enhancers based on their sequence information, however, the resulting performance still requires improvements. Using deep learning methods, this study proposes a model ensemble of classifiers for predicting enhancers based on deep recurrent neural networks. The input features of deep ensemble networks were generated from six types of dinucleotide physicochemical properties, which had outperformed the other features. In summary, our model which used this ensemble approach could identify enhancers with achieved sensitivity of 75.5%, specificity of 76%, accuracy of 75.5%, and MCC of 0.51. For classifying enhancers into strong or weak sequences, our model reached sensitivity of 83.15%, specificity of 45.61%, accuracy of 68.49%, and MCC of 0.312. Compared to the benchmark result, our results had higher performance in term of most measurement metrics. The results showed that deep model ensembles hold the potential for improving on the best results achieved to date using shallow machine learning methods.
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Affiliation(s)
- Kok Keng Tan
- Institute of Systems Science, 25 Heng Mui Keng Terrace, National University of Singapore, Singapore 119615, Singapore
| | - Nguyen Quoc Khanh Le
- Medical Humanities Research Cluster, School of Humanities, Nanyang Technological University, 48 Nanyang Ave, Singapore 639798, Singapore
| | - Hui-Yuan Yeh
- Medical Humanities Research Cluster, School of Humanities, Nanyang Technological University, 48 Nanyang Ave, Singapore 639798, Singapore.
| | - Matthew Chin Heng Chua
- Institute of Systems Science, 25 Heng Mui Keng Terrace, National University of Singapore, Singapore 119615, Singapore.
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12
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Abstract
Background:DNA-binding proteins, binding to DNA, widely exist in living cells, participating in many cell activities. They can participate some DNA-related cell activities, for instance DNA replication, transcription, recombination, and DNA repair.Objective:Given the importance of DNA-binding proteins, studies for predicting the DNA-binding proteins have been a popular issue over the past decades. In this article, we review current machine-learning methods which research on the prediction of DNA-binding proteins through feature representation methods, classifiers, measurements, dataset and existing web server.Method:The prediction methods of DNA-binding protein can be divided into two types, based on amino acid composition and based on protein structure. In this article, we accord to the two types methods to introduce the application of machine learning in DNA-binding proteins prediction.Results:Machine learning plays an important role in the classification of DNA-binding proteins, and the result is better. The best ACC is above 80%.Conclusion:Machine learning can be widely used in many aspects of biological information, especially in protein classification. Some issues should be considered in future work. First, the relationship between the number of features and performance must be explored. Second, many features are used to predict DNA-binding proteins and propose solutions for high-dimensional spaces.
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Affiliation(s)
- Kaiyang Qu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- College of Intelligence and Computing, Tianjin University, Tianjin, China
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13
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Zhang M, Xu Y, Li L, Liu Z, Yang X, Yu DJ. Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble. Anal Biochem 2018; 550:41-48. [DOI: 10.1016/j.ab.2018.03.027] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Revised: 03/27/2018] [Accepted: 03/28/2018] [Indexed: 11/25/2022]
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14
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A Two-Layer Computational Model for Discrimination of Enhancer and Their Types Using Hybrid Features Pace of Pseudo K-Tuple Nucleotide Composition. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2017. [DOI: 10.1007/s13369-017-2818-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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15
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Tahir M, Hayat M, Kabir M. Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 146:69-75. [PMID: 28688491 DOI: 10.1016/j.cmpb.2017.05.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 05/05/2017] [Accepted: 05/19/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVES Enhancers are pivotal DNA elements, which are widely used in eukaryotes for activation of transcription genes. On the basis of enhancer strength, they are further classified into two groups; strong enhancers and weak enhancers. Due to high availability of huge amount of DNA sequences, it is needed to develop fast, reliable and robust intelligent computational method, which not only identify enhancers but also determines their strength. Considerable progress has been achieved in this regard; however, timely and precisely identification of enhancers is still a challenging task. METHODS Two-level intelligent computational model for identification of enhancers and their subgroups is proposed. Two different feature extraction techniques including di-nucleotide composition and tri-nucleotide composition were adopted for extraction of numerical descriptors. Four classification methods including probabilistic neural network, support vector machine, k-nearest neighbor and random forest were utilized for classification. RESULTS The proposed method yielded 77.25% of accuracy for dataset S1 contains enhancers and non-enhancers, whereas 64.70% of accuracy for dataset S2 comprises of strong enhancer and weak enhancer sequences using jackknife cross-validation test. CONCLUSION The predictive results validated that the proposed method is better than that of existing approaches so far reported in the literature. It is thus highly observed that the developed method will be useful and expedient for basic research and academia.
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Affiliation(s)
- Muhammad Tahir
- Department of Computer Science, Abdul Wali Khan University Mardan, KP Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, KP Pakistan.
| | - Muhammad Kabir
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
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16
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Tung CH, Chen CW, Sun HH, Chu YW. Predicting human protein subcellular localization by heterogeneous and comprehensive approaches. PLoS One 2017; 12:e0178832. [PMID: 28658305 PMCID: PMC5489166 DOI: 10.1371/journal.pone.0178832] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 05/19/2017] [Indexed: 11/19/2022] Open
Abstract
Drug development and investigation of protein function both require an understanding of protein subcellular localization. We developed a system, REALoc, that can predict the subcellular localization of singleplex and multiplex proteins in humans. This system, based on comprehensive strategy, consists of two heterogeneous systematic frameworks that integrate one-to-one and many-to-many machine learning methods and use sequence-based features, including amino acid composition, surface accessibility, weighted sign aa index, and sequence similarity profile, as well as gene ontology function-based features. REALoc can be used to predict localization to six subcellular compartments (cell membrane, cytoplasm, endoplasmic reticulum/Golgi, mitochondrion, nucleus, and extracellular). REALoc yielded a 75.3% absolute true success rate during five-fold cross-validation and a 57.1% absolute true success rate in an independent database test, which was >10% higher than six other prediction systems. Lastly, we analyzed the effects of Vote and GANN models on singleplex and multiplex localization prediction efficacy. REALoc is freely available at http://predictor.nchu.edu.tw/REALoc.
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Affiliation(s)
- Chi-Hua Tung
- Department of Bioinformatics, Chung-Hua University, Hsinchu, Taiwan
| | - Chi-Wei Chen
- Institute of Genomics and Bioinformatics, National Chung Hsing University 250, Taichung 402, Taiwan
| | - Han-Hao Sun
- Institute of Genomics and Bioinformatics, National Chung Hsing University 250, Taichung 402, Taiwan
| | - Yen-Wei Chu
- Institute of Genomics and Bioinformatics, National Chung Hsing University 250, Taichung 402, Taiwan
- Biotechnology Center, Agricultural Biotechnology Center, Institute of Molecular Biology, Graduate Institute of Biotechnology, National Chung Hsing University 250, Taichung 402, Taiwan
- * E-mail:
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