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Hwang H, Jeon H, Yeo N, Baek D. Big data and deep learning for RNA biology. Exp Mol Med 2024; 56:1293-1321. [PMID: 38871816 PMCID: PMC11263376 DOI: 10.1038/s12276-024-01243-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.
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Affiliation(s)
- Hyeonseo Hwang
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyeonseong Jeon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Genome4me Inc., Seoul, Republic of Korea
| | - Nagyeong Yeo
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Daehyun Baek
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Genome4me Inc., Seoul, Republic of Korea.
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2
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A survey on gene expression data analysis using deep learning methods for cancer diagnosis. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 177:1-13. [PMID: 35988771 DOI: 10.1016/j.pbiomolbio.2022.08.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/09/2022] [Accepted: 08/12/2022] [Indexed: 02/07/2023]
Abstract
Gene Expression Data is the biological data to extract meaningful hidden information from the gene dataset. This gene information is used for disease diagnosis especially in cancer treatment based on the variations in gene expression levels. DNA microarray is an efficient method for gene expression classification and prediction of cancer disease for specific types of cancer. Due to the abundance of computing power, deep learning (DL) has become a widespread technique in the healthcare sector. The gene expression dataset has a limited number of samples but a large number of features. Data augmentation is needed for gene expression datasets to overcome the dimensionality problem in gene data. It is a technique to generating the synthetic samples to increase the diversity of data. Deep learning methods are designed to learn and extract the features that come from the raw input data in the form of multidimensional arrays. This paper reviews the existing research in deep learning techniques like Feed Forward Neural Network (FFN), Convolutional Neural Network (CNN), Autoencoder (AE) and Recurrent Neural Network (RNN) for the classification and prediction of cancer disease and its types through gene expression data analysis.
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3
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Dong B, Li M, Jiang B, Gao B, Li D, Zhang T. Antimicrobial Peptides Prediction method based on sequence multidimensional feature embedding. Front Genet 2022; 13:1069558. [PMID: 36468005 PMCID: PMC9714691 DOI: 10.3389/fgene.2022.1069558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/02/2022] [Indexed: 09/10/2024] Open
Abstract
Antimicrobial peptides (AMPs) are alkaline substances with efficient bactericidal activity produced in living organisms. As the best substitute for antibiotics, they have been paid more and more attention in scientific research and clinical application. AMPs can be produced from almost all organisms and are capable of killing a wide variety of pathogenic microorganisms. In addition to being antibacterial, natural AMPs have many other therapeutically important activities, such as wound healing, antioxidant and immunomodulatory effects. To discover new AMPs, the use of wet experimental methods is expensive and difficult, and bioinformatics technology can effectively solve this problem. Recently, some deep learning methods have been applied to the prediction of AMPs and achieved good results. To further improve the prediction accuracy of AMPs, this paper designs a new deep learning method based on sequence multidimensional representation. By encoding and embedding sequence features, and then inputting the model to identify AMPs, high-precision classification of AMPs and Non-AMPs with lengths of 10-200 is achieved. The results show that our method improved accuracy by 1.05% compared to the most advanced model in independent data validation without decreasing other indicators.
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Affiliation(s)
- Benzhi Dong
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Mengna Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Bei Jiang
- Tianjin Second People's Hospital, Tianjin Institute of Hepatology, Tianjin, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Dan Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
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4
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Qin R, Mahal LK, Bojar D. Deep learning explains the biology of branched glycans from single-cell sequencing data. iScience 2022; 25:105163. [PMID: 36217547 PMCID: PMC9547197 DOI: 10.1016/j.isci.2022.105163] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 09/06/2022] [Accepted: 09/16/2022] [Indexed: 11/03/2022] Open
Abstract
Glycosylation is ubiquitous and often dysregulated in disease. However, the regulation and functional significance of various types of glycosylation at cellular levels is hard to unravel experimentally. Multi-omics, single-cell measurements such as SUGAR-seq, which quantifies transcriptomes and cell surface glycans, facilitate addressing this issue. Using SUGAR-seq data, we pioneered a deep learning model to predict the glycan phenotypes of cells (mouse T lymphocytes) from transcripts, with the example of predicting β1,6GlcNAc-branching across T cell subtypes (test set F1 score: 0.9351). Model interpretation via SHAP (SHapley Additive exPlanations) identified highly predictive genes, in part known to impact (i) branched glycan levels and (ii) the biology of branched glycans. These genes included physiologically relevant low-abundance genes that were not captured by conventional differential expression analysis. Our work shows that interpretable deep learning models are promising for uncovering novel functions and regulatory mechanisms of glycans from integrated transcriptomic and glycomic datasets.
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Affiliation(s)
- Rui Qin
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, Canada
| | - Lara K. Mahal
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, Canada
| | - Daniel Bojar
- Department of Chemistry and Molecular Biology, University of Gothenburg, 405 30 Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 405 30 Gothenburg, Sweden
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5
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Xu D, Liu B, Wang J, Zhang Z. Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers. Front Bioeng Biotechnol 2022; 10:998298. [PMID: 36277390 PMCID: PMC9585160 DOI: 10.3389/fbioe.2022.998298] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background: In the biotechnology and applied microbiology sectors, artificial intelligence (AI) has been extensively used in disease diagnostics, drug research and development, functional genomics, biomarker recognition, and medical imaging diagnostics. In our study, from 2000 to 2021, science publications focusing on AI in biotechnology were reviewed, and quantitative, qualitative, and modeling analyses were performed. Methods: On 6 May 2022, the Web of Science Core Collection (WoSCC) was screened for AI applications in biotechnology and applied microbiology; 3,529 studies were identified between 2000 and 2022, and analyzed. The following information was collected: publication, country or region, references, knowledgebase, institution, keywords, journal name, and research hotspots, and examined using VOSviewer and CiteSpace V bibliometric platforms. Results: We showed that 128 countries published articles related to AI in biotechnology and applied microbiology; the United States had the most publications. In addition, 584 global institutions contributed to publications, with the Chinese Academy of Science publishing the most. Reference clusters from studies were categorized into ten headings: deep learning, prediction, support vector machines (SVM), object detection, feature representation, synthetic biology, amyloid, human microRNA precursors, systems biology, and single cell RNA-Sequencing. Research frontier keywords were represented by microRNA (2012–2020) and protein-protein interactions (PPIs) (2012–2020). Conclusion: We systematically, objectively, and comprehensively analyzed AI-related biotechnology and applied microbiology literature, and additionally, identified current hot spots and future trends in this area. Our review provides researchers with a comprehensive overview of the dynamic evolution of AI in biotechnology and applied microbiology and identifies future key research areas.
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Affiliation(s)
- Dongyu Xu
- Department of Computer, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Bing Liu
- Department of Bone Oncology, The People’s Hospital of Liaoning Province, Shenyang, Liaoning, China
| | - Jian Wang
- Department of Pathogenic Biology, School of Basic Medicine, China Medical University, Shenyang, Liaoning, China
| | - Zhichang Zhang
- Department of Computer, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
- *Correspondence: Zhichang Zhang,
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6
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Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction. Symmetry (Basel) 2022. [DOI: 10.3390/sym14071302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms, have been widely applied to RRI prediction with remarkable success. This paper first gives a brief introduction to the traditional machine learning methods applied on RRI prediction and benchmark databases for training the models, and then provides a recent methodology overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions.
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Chen Z, Wang Z, Du M, Liu Z. Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022; 41:1343-1353. [PMID: 34524706 PMCID: PMC9292970 DOI: 10.1002/jum.15827] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 08/03/2021] [Accepted: 08/16/2021] [Indexed: 05/27/2023]
Abstract
The incidence of infertility is continuously increasing nearly all over the world in recent years, and novel methods for accurate assessment are of great need. Artificial Intelligence (AI) has gradually become an effective supplementary method for the assessment of female reproductive function. It has been used in clinical follicular monitoring, optimum timing for transplantation, and prediction of pregnancy outcome. Some literatures summarize the use of AI in this field, but few of them focus on the assessment of female reproductive function by AI-aided ultrasound. In this review, we mainly discussed the applicability, feasibility, and value of clinical application of AI in ultrasound to monitor follicles, assess endometrial receptivity, and predict the pregnancy outcome of in vitro fertilization and embryo transfer (IVF-ET). The limitations, challenges, and future trends of ultrasound combined with AI in providing efficient and individualized evaluation of female reproductive function had also been mentioned.
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Affiliation(s)
- Zhiyi Chen
- The First Affiliated Hospital, Medical Imaging Center, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
- Institute of Medical ImagingUniversity of South ChinaHengyangChina
| | - Ziyao Wang
- The First Affiliated Hospital, Medical Imaging Center, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
| | - Meng Du
- Institute of Medical ImagingUniversity of South ChinaHengyangChina
| | - Zhenyu Liu
- The First Affiliated Hospital, Medical Imaging Center, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
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8
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Multi-channel CNN based anticancer peptides identification. Anal Biochem 2022; 650:114707. [PMID: 35568159 DOI: 10.1016/j.ab.2022.114707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/27/2022] [Accepted: 04/27/2022] [Indexed: 11/20/2022]
Abstract
Cancer is one of the most dangerous diseases in the world that often leads to misery and death. Current treatments include different kinds of anticancer therapy which exhibit different types of side effects. Because of certain physicochemical properties, anticancer peptides (ACPs) have opened a new path of treatments for this deadly disease. That is why a well-performed methodology for identifying novel anticancer peptides has great importance in the fight against cancer. In addition to the laboratory techniques, various machine learning and deep learning methodologies have developed in recent years for this task. Although these models have shown reasonable predictive ability, there's still room for improvement in terms of performance and exploring new types of algorithms. In this work, we have proposed a novel multi-channel convolutional neural network (CNN) for identifying anticancer peptides from protein sequences. We have collected data from the existing state-of-the-art methodologies and applied binary encoding for data preprocessing. We have also employed k-fold cross-validation to train our models on benchmark datasets and compared our models' performance on the independent datasets. The comparison has indicated our models' superiority on various evaluation metrics. We think our work can be a valuable asset in finding novel anticancer peptides. We have provided a user-friendly web server for academic purposes and it is publicly available at: \texttt{http://103.99.176.239/iacp-cnn/}.
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9
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Machine Learning Based Methods and Best Practices of microRNA-Target Prediction and Validation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1385:109-131. [DOI: 10.1007/978-3-031-08356-3_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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10
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Wang H, Zhao J, Zhao H, Li H, Wang J. CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model. BMC Bioinformatics 2021; 22:512. [PMID: 34670488 PMCID: PMC8527680 DOI: 10.1186/s12859-021-04433-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/05/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Anticancer peptides are defence substances with innate immune functions that can selectively act on cancer cells without harming normal cells and many studies have been conducted to identify anticancer peptides. In this paper, we introduce the anticancer peptide secondary structures as additional features and propose an effective computational model, CL-ACP, that uses a combined network and attention mechanism to predict anticancer peptides. RESULTS The CL-ACP model uses secondary structures and original sequences of anticancer peptides to construct the feature space. The long short-term memory and convolutional neural network are used to extract the contextual dependence and local correlations of the feature space. Furthermore, a multi-head self-attention mechanism is used to strengthen the anticancer peptide sequences. Finally, three categories of feature information are classified by cascading. CL-ACP was validated using two types of datasets, anticancer peptide datasets and antimicrobial peptide datasets, on which it achieved good results compared to previous methods. CL-ACP achieved the highest AUC values of 0.935 and 0.972 on the anticancer peptide and antimicrobial peptide datasets, respectively. CONCLUSIONS CL-ACP can effectively recognize antimicrobial peptides, especially anticancer peptides, and the parallel combined neural network structure of CL-ACP does not require complex feature design and high time cost. It is suitable for application as a useful tool in antimicrobial peptide design.
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Affiliation(s)
- Huiqing Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jian Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Hong Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Haolin Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Juan Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
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11
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Khatun MS, Alam MA, Shoombuatong W, Mollah MNH, Kurata H, Hasan MM. Recent development of bioinformatics tools for microRNA target prediction. Curr Med Chem 2021; 29:865-880. [PMID: 34348604 DOI: 10.2174/0929867328666210804090224] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 06/10/2021] [Accepted: 06/15/2021] [Indexed: 11/22/2022]
Abstract
MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.
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Affiliation(s)
- Mst Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502. Japan
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112. United States
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700. Thailand
| | - Md Nurul Haque Mollah
- Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh. 5Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083. Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502. Japan
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502. Japan
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12
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Abstract
miRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.
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Affiliation(s)
- Abdulkadir Tasdelen
- TOBB Technical Sciences Vocational School, Karabuk University, Karabuk, Turkey.
| | - Baha Sen
- Department of Computer Engineering, Ankara Yıldırım Beyazıt University, Ankara, Turkey
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13
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Aziz AZB, Hasan MAM, Shin J. Identification of RNA pseudouridine sites using deep learning approaches. PLoS One 2021; 16:e0247511. [PMID: 33621235 PMCID: PMC7901771 DOI: 10.1371/journal.pone.0247511] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 02/08/2021] [Indexed: 01/05/2023] Open
Abstract
Pseudouridine(Ψ) is widely popular among various RNA modifications which have been confirmed to occur in rRNA, mRNA, tRNA, and nuclear/nucleolar RNA. Hence, identifying them has vital significance in academic research, drug development and gene therapies. Several laboratory techniques for Ψ identification have been introduced over the years. Although these techniques produce satisfactory results, they are costly, time-consuming and requires skilled experience. As the lengths of RNA sequences are getting longer day by day, an efficient method for identifying pseudouridine sites using computational approaches is very important. In this paper, we proposed a multi-channel convolution neural network using binary encoding. We employed k-fold cross-validation and grid search to tune the hyperparameters. We evaluated its performance in the independent datasets and found promising results. The results proved that our method can be used to identify pseudouridine sites for associated purposes. We have also implemented an easily accessible web server at http://103.99.176.239/ipseumulticnn/.
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Affiliation(s)
- Abu Zahid Bin Aziz
- Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
- * E-mail:
| | - Md. Al Mehedi Hasan
- Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
| | - Jungpil Shin
- School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Japan
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