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Jin W, Jia J, Si Y, Liu J, Li H, Zhu H, Wu Z, Zuo Y, Yu L. Identification of Key lncRNAs Associated with Immune Infiltration and Prognosis in Gastric Cancer. Biochem Genet 2024:10.1007/s10528-024-10801-w. [PMID: 38658494 DOI: 10.1007/s10528-024-10801-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 04/05/2024] [Indexed: 04/26/2024]
Abstract
Long non-coding RNAs (lncRNAs), as promising novel biomarkers for cancer treatment and prognosis, can function as tumor suppressors and oncogenes in the occurrence and development of many types of cancer, including gastric cancer (GC). However, little is known about the complex regulatory system of lncRNAs in GC. In this study, we systematically analyzed lncRNA and miRNA transcriptomic profiles of GC based on bioinformatics methods and experimental validation. An lncRNA-miRNA interaction network related to GC was constructed, and the nine crucial lncRNAs were identified. These 9 lncRNAs were found to be associated with the prognosis of GC patients by Cox proportional hazards regression analysis. Among them, the expression of lncRNA SNHG14 can affect the survival of GC patients as a potential prognostic marker. Moreover, it was shown that SNHG14 was involved in immune-related pathways and significantly correlated with immune cell infiltration in GC. Meanwhile, we found that SNHG14 affected immune function in many cancers, such as breast cancer and esophageal carcinoma. Such information revealed that SNHG14 may serve as a potential target for cancer immunotherapy. As well, our study could provide practical and theoretical guiding significance for clinical application of non-coding RNAs.
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Affiliation(s)
- Wen Jin
- Clinical Medical Research Center, Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolia People's Hospital, Hohhot, 010010, China
| | - Jianchao Jia
- Clinical Medical Research Center, Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolia People's Hospital, Hohhot, 010010, China
| | - Yangming Si
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
| | - Jianli Liu
- School of Water Resource and Environment Engineering, China University of Geosciences, Beijing, 100083, China
| | - Hanshuang Li
- College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Hao Zhu
- Clinical Medical Research Center, Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolia People's Hospital, Hohhot, 010010, China
| | - Zhouying Wu
- Clinical Medical Research Center, Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolia People's Hospital, Hohhot, 010010, China
| | - Yongchun Zuo
- College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
- Digital College, Inner Mongolia Intelligent Union Big Data Academy, Hohhot, 010010, China.
- Inner Mongolia International Mongolian Hospital, Hohhot, 010065, China.
| | - Lan Yu
- Clinical Medical Research Center, Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolia People's Hospital, Hohhot, 010010, China.
- Department of Endocrine and Metabolic Diseases, Inner Mongolia People's Hospital, Hohhot, 010010, China.
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Asim MN, Ibrahim MA, Zehe C, Trygg J, Dengel A, Ahmed S. BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA–miRNA interaction prediction. Interdiscip Sci 2022; 14:841-862. [PMID: 35947255 PMCID: PMC9581873 DOI: 10.1007/s12539-022-00535-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 06/16/2022] [Accepted: 07/12/2022] [Indexed: 11/30/2022]
Abstract
Background and objective: Interactions of long non-coding ribonucleic acids (lncRNAs) with micro-ribonucleic acids (miRNAs) play an essential role in gene regulation, cellular metabolic, and pathological processes. Existing purely sequence based computational approaches lack robustness and efficiency mainly due to the high length variability of lncRNA sequences. Hence, the prime focus of the current study is to find optimal length trade-offs between highly flexible length lncRNA sequences. Method The paper at hand performs in-depth exploration of diverse copy padding, sequence truncation approaches, and presents a novel idea of utilizing only subregions of lncRNA sequences to generate fixed-length lncRNA sequences. Furthermore, it presents a novel bag of tricks-based deep learning approach “Bot-Net” which leverages a single layer long-short-term memory network regularized through DropConnect to capture higher order residue dependencies, pooling to retain most salient features, normalization to prevent exploding and vanishing gradient issues, learning rate decay, and dropout to regularize precise neural network for lncRNA–miRNA interaction prediction. Results BoT-Net outperforms the state-of-the-art lncRNA–miRNA interaction prediction approach by 2%, 8%, and 4% in terms of accuracy, specificity, and matthews correlation coefficient. Furthermore, a case study analysis indicates that BoT-Net also outperforms state-of-the-art lncRNA–protein interaction predictor on a benchmark dataset by accuracy of 10%, sensitivity of 19%, specificity of 6%, precision of 14%, and matthews correlation coefficient of 26%. Conclusion In the benchmark lncRNA–miRNA interaction prediction dataset, the length of the lncRNA sequence varies from 213 residues to 22,743 residues and in the benchmark lncRNA–protein interaction prediction dataset, lncRNA sequences vary from 15 residues to 1504 residues. For such highly flexible length sequences, fixed length generation using copy padding introduces a significant level of bias which makes a large number of lncRNA sequences very much identical to each other and eventually derail classifier generalizeability. Empirical evaluation reveals that within 50 residues of only the starting region of long lncRNA sequences, a highly informative distribution for lncRNA–miRNA interaction prediction is contained, a crucial finding exploited by the proposed BoT-Net approach to optimize the lncRNA fixed length generation process. Availability: BoT-Net web server can be accessed at https://sds_genetic_analysis.opendfki.de/lncmiRNA/. Graphic Abstract ![]()
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Affiliation(s)
- Muhammad Nabeel Asim
- Department of Computer Science, Technical University of Kaiserslautern, 67663, Kaiserslautern, Rhineland-Palatinate, Germany.
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Rhineland-Palatinate, Germany.
| | - Muhammad Ali Ibrahim
- Department of Computer Science, Technical University of Kaiserslautern, 67663, Kaiserslautern, Rhineland-Palatinate, Germany
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Rhineland-Palatinate, Germany
| | - Christoph Zehe
- Sartorius Stedim Cellca GmbH, 88471, Laupheim, Baden-Wurttemberg, Germany
| | - Johan Trygg
- Sartorius Stedim Cellca GmbH, 88471, Laupheim, Baden-Wurttemberg, Germany
- Computational Life Science Cluster (CLiC), Umea University, 90187, Umea, Sweden
| | - Andreas Dengel
- Department of Computer Science, Technical University of Kaiserslautern, 67663, Kaiserslautern, Rhineland-Palatinate, Germany
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Rhineland-Palatinate, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Rhineland-Palatinate, Germany
- Computational Life Science Cluster (CLiC), Umea University, 90187, Umea, Sweden
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3
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Gong Y, Zhu W, Sun M, Shi L. Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview. Front Genet 2021; 12:813873. [PMID: 34956340 PMCID: PMC8692768 DOI: 10.3389/fgene.2021.813873] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 11/26/2021] [Indexed: 12/30/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) are usually located in the nucleus and cytoplasm of cells. The transcripts of lncRNAs are >200 nucleotides in length and do not encode proteins. Compared with small RNAs, lncRNAs have longer sequences, more complex spatial structures, and more diverse and complex mechanisms involved in the regulation of gene expression. LncRNAs are widely involved in the biological processes of cells, and in the occurrence and development of many human diseases. Many studies have shown that lncRNAs can induce the occurrence of diseases, and some lncRNAs undergo specific changes in tumor cells. Research into the roles of lncRNAs has covered the diagnosis of, for example, cardiovascular, cerebrovascular, and central nervous system diseases. The bioinformatics of lncRNAs has gradually become a research hotspot and has led to the discovery of a large number of lncRNAs and associated biological functions, and lncRNA databases and recognition models have been developed. In this review, the research progress of lncRNAs is discussed, and lncRNA-related databases and the mechanisms and modes of action of lncRNAs are described. In addition, disease-related lncRNA methods and the relationships between lncRNAs and human lung adenocarcinoma, rectal cancer, colon cancer, heart disease, and diabetes are discussed. Finally, the significance and existing problems of lncRNA research are considered.
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Affiliation(s)
- Yuxin Gong
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Wen Zhu
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Meili Sun
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Lei Shi
- Department of Spine Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
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4
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LRGCPND: Predicting Associations between ncRNA and Drug Resistance via Linear Residual Graph Convolution. Int J Mol Sci 2021; 22:ijms221910508. [PMID: 34638849 PMCID: PMC8508984 DOI: 10.3390/ijms221910508] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/25/2021] [Accepted: 09/27/2021] [Indexed: 01/08/2023] Open
Abstract
Accurate inference of the relationship between non-coding RNAs (ncRNAs) and drug resistance is essential for understanding the complicated mechanisms of drug actions and clinical treatment. Traditional biological experiments are time-consuming, laborious, and minor in scale. Although several databases provide relevant resources, computational method for predicting this type of association has not yet been developed. In this paper, we leverage the verified association data of ncRNA and drug resistance to construct a bipartite graph and then develop a linear residual graph convolution approach for predicting associations between non-coding RNA and drug resistance (LRGCPND) without introducing or defining additional data. LRGCPND first aggregates the potential features of neighboring nodes per graph convolutional layer. Next, we transform the information between layers through a linear function. Eventually, LRGCPND unites the embedding representations of each layer to complete the prediction. Results of comparison experiments demonstrate that LRGCPND has more reliable performance than seven other state-of-the-art approaches with an average AUC value of 0.8987. Case studies illustrate that LRGCPND is an effective tool for inferring the associations between ncRNA and drug resistance.
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Yang H, Qi C, Li B, Cheng L. Non-coding RNAs as Novel Biomarkers in Cancer Drug Resistance. Curr Med Chem 2021; 29:837-848. [PMID: 34348605 DOI: 10.2174/0929867328666210804090644] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/09/2021] [Accepted: 06/15/2021] [Indexed: 11/22/2022]
Abstract
Chemotherapy is often the primary and most effective anticancer treatment; however, drug resistance remains a major obstacle to it being curative. Recent studies have demonstrated that non-coding RNAs (ncRNAs), especially microRNAs and long non-coding RNAs, are involved in drug resistance of tumor cells in many ways, such as modulation of apoptosis, drug efflux and metabolism, epithelial-to-mesenchymal transition, DNA repair, and cell cycle progression. Exploring the relationships between ncRNAs and drug resistance will not only contribute to our understanding of the mechanisms of drug resistance and provide ncRNA biomarkers of chemoresistance, but will also help realize personalized anticancer treatment regimens. Due to the high cost and low efficiency of biological experimentation, many researchers have opted to use computational methods to identify ncRNA biomarkers associated with drug resistance. In this review, we summarize recent discoveries related to ncRNA-mediated drug resistance and highlight the computational methods and resources available for ncRNA biomarkers involved in chemoresistance.
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Affiliation(s)
- Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081. China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081. China
| | - Boyan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081. China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081. China
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6
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Lv Y, Huang S, Zhang T, Gao B. Application of Multilayer Network Models in Bioinformatics. Front Genet 2021; 12:664860. [PMID: 33868392 PMCID: PMC8044439 DOI: 10.3389/fgene.2021.664860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 02/26/2021] [Indexed: 11/24/2022] Open
Abstract
Multilayer networks provide an efficient tool for studying complex systems, and with current, dramatic development of bioinformatics tools and accumulation of data, researchers have applied network concepts to all aspects of research problems in the field of biology. Addressing the combination of multilayer networks and bioinformatics, through summarizing the applications of multilayer network models in bioinformatics, this review classifies applications and presents a summary of the latest results. Among them, we classify the applications of multilayer networks according to the object of study. Furthermore, because of the systemic nature of biology, we classify the subjects into several hierarchical categories, such as cells, tissues, organs, and groups, according to the hierarchical nature of biological composition. On the basis of the complexity of biological systems, we selected brain research for a detailed explanation. We describe the application of multilayer networks and chronological networks in brain research to demonstrate the primary ideas associated with the application of multilayer networks in biological studies. Finally, we mention a quality assessment method focusing on multilayer and single-layer networks as an evaluation method emphasizing network studies.
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Affiliation(s)
- Yuanyuan Lv
- Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, China
- Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
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Xu L, Jiao S, Zhang D, Wu S, Zhang H, Gao B. Identification of long noncoding RNAs with machine learning methods: a review. Brief Funct Genomics 2021; 20:174-180. [PMID: 33758917 DOI: 10.1093/bfgp/elab017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 02/24/2021] [Accepted: 02/25/2021] [Indexed: 12/11/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length greater than 200 nucleotides. Studies have shown that they play an important role in many life activities. Dozens of lncRNAs have been characterized to some extent, and they are reported to be related to the development of diseases in a variety of cells. However, the biological functions of most lncRNAs are currently still unclear. Therefore, accurately identifying and predicting lncRNAs would be helpful for research on their biological functions. Due to the disadvantages of high cost and high resource-intensiveness of experimental methods, scientists have developed numerous computational methods to identify and predict lncRNAs in recent years. In this paper, we systematically summarize the machine learning-based lncRNAs prediction tools from several perspectives, and discuss the challenges and prospects for the future work.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic
| | - Shihu Jiao
- College of Chemistry, Sichuan University, Sichuan, China
| | - Dandan Zhang
- Departments of Obstetrics and Gynecology, First Affiliated Hospital of Harbin Medical University
| | - Song Wu
- Preventive Treatment of Disease Centre of Qinhuangdao Hospital of Traditional Chinese Medicine
| | - Haihong Zhang
- First Affiliated Hospital of Harbin Medical University
| | - Bo Gao
- Second Affiliated Hospital, Harbin Medical University, Harbin, China
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8
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Xu X, Liu S, Yang Z, Zhao X, Deng Y, Zhang G, Pang J, Zhao C, Zhang W. A systematic review of computational methods for predicting long noncoding RNAs. Brief Funct Genomics 2021; 20:162-173. [PMID: 33754153 DOI: 10.1093/bfgp/elab016] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/20/2021] [Accepted: 02/22/2021] [Indexed: 12/20/2022] Open
Abstract
Accurately and rapidly distinguishing long noncoding RNAs (lncRNAs) from transcripts is prerequisite for exploring their biological functions. In recent years, many computational methods have been developed to predict lncRNAs from transcripts, but there is no systematic review on these computational methods. In this review, we introduce databases and features involved in the development of computational prediction models, and subsequently summarize existing state-of-the-art computational methods, including methods based on binary classifiers, deep learning and ensemble learning. However, a user-friendly way of employing existing state-of-the-art computational methods is in demand. Therefore, we develop a Python package ezLncPred, which provides a pragmatic command line implementation to utilize nine state-of-the-art lncRNA prediction methods. Finally, we discuss challenges of lncRNA prediction and future directions.
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9
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Zhang S, Amahong K, Sun X, Lian X, Liu J, Sun H, Lou Y, Zhu F, Qiu Y. The miRNA: a small but powerful RNA for COVID-19. Brief Bioinform 2021; 22:1137-1149. [PMID: 33675361 PMCID: PMC7989616 DOI: 10.1093/bib/bbab062] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/05/2021] [Accepted: 02/08/2021] [Indexed: 12/12/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a severe and rapidly evolving epidemic. Now, although a few drugs and vaccines have been proved for its treatment and prevention, little systematic comments are made to explain its susceptibility to humans. A few scattered studies used bioinformatics methods to explore the role of microRNA (miRNA) in COVID-19 infection. Combining these timely reports and previous studies about virus and miRNA, we comb through the available clues and seemingly make the perspective reasonable that the COVID-19 cleverly exploits the interplay between the small miRNA and other biomolecules to avoid being effectively recognized and attacked from host immune protection as well to deactivate functional genes that are crucial for immune system. In detail, SARS-CoV-2 can be regarded as a sponge to adsorb host immune-related miRNA, which forces host fall into dysfunction status of immune system. Besides, SARS-CoV-2 encodes its own miRNAs, which can enter host cell and are not perceived by the host's immune system, subsequently targeting host function genes to cause illnesses. Therefore, this article presents a reasonable viewpoint that the miRNA-based interplays between the host and SARS-CoV-2 may be the primary cause that SARS-CoV-2 accesses and attacks the host cells.
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Affiliation(s)
- Song Zhang
- College of Pharmaceutical Sciences in Zhejiang University and the First Affiliated Hospital of Zhejiang University School of Medicine, China
| | | | - Xiuna Sun
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Xichen Lian
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Jin Liu
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Yan Lou
- Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, the First Affiliated Hospital, Zhejiang University School of Medicine, China
| | - Feng Zhu
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, the First Affiliated Hospital, Zhejiang University School of Medicine, China
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10
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Jiao S, Wu S, Huang S, Liu M, Gao B. Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases. Front Genet 2021; 12:665233. [PMID: 33815488 PMCID: PMC8017306 DOI: 10.3389/fgene.2021.665233] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 03/01/2021] [Indexed: 12/14/2022] Open
Abstract
Circular RNAs (circRNAs) are a class of endogenous non-coding RNAs (ncRNAs) with a closed-loop structure that are mainly produced by variable processing of precursor mRNAs (pre-mRNAs). They are widely present in all eukaryotes and are very stable. Currently, circRNA studies have become a hotspot in RNA research. It has been reported that circRNAs constitute a significant proportion of transcript expression, and some are significantly more abundantly expressed than other transcripts. CircRNAs have regulatory roles in gene expression and critical biological functions in the development of organisms, such as acting as microRNA sponges or as endogenous RNAs and biomarkers. As such, they may have useful functions in the diagnosis and treatment of diseases. CircRNAs have been found to play an important role in the development of several diseases, including atherosclerosis, neurological disorders, diabetes, and cancer. In this paper, we review the status of circRNA research, describe circRNA-related databases and the identification of circRNAs, discuss the role of circRNAs in human diseases such as colon cancer, atherosclerosis, and gastric cancer, and identify remaining research questions related to circRNAs.
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Affiliation(s)
- Shihu Jiao
- Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Song Wu
- Director of Preventive Treatment of Disease Centre, Qinhuangdao Hospital of Traditional Chinese Medicine, Qinhuangdao, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Mingyang Liu
- Department of Internal Medicine-Oncology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
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Wang W, Guan X, Khan MT, Xiong Y, Wei DQ. LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions. Comput Biol Chem 2020; 89:107406. [PMID: 33120126 DOI: 10.1016/j.compbiolchem.2020.107406] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/12/2020] [Accepted: 10/15/2020] [Indexed: 02/07/2023]
Abstract
The interactions between miRNAs and long non-coding RNAs (lncRNAs) are subject to intensive recent studies due to its critical role in gene regulations. Computational prediction of lncRNA-miRNA interactions has become a popular alternative strategy to the experimental methods for identification of underlying interactions. It is desirable to develop the machine learning-based models for prediction of lncRNA-miRNA based on the experimentally validated interactions between lncRNAs and miRNAs. The accuracy and robustness of existing models based on machine learning techniques are subject to further improvement. Considering that the attributes of lncRNA and miRNA contribute key importance in the interaction between these two RNAs, a deep learning model, named LMI-DForest, is proposed here by combining the deep forest and autoencoder strategies. Systematic comparison on the experiment validated datasets for lncRNA-miRNA interaction datasets demonstrates that the proposed method consistently shows superior performance over the other machine learning models in the lncRNA-miRNA interaction prediction.
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Affiliation(s)
- Wei Wang
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoqing Guan
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Muhammad Tahir Khan
- Institute of Molecular Biology and Biotechnology, The University of Lahore Pakistan, Pakistan
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Peng Cheng Laboratory, Shenzhen, Guangdong, China.
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