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Li X, Qu W, Yan J, Tan J. RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction. J Chem Inf Model 2024; 64:2221-2235. [PMID: 37158609 DOI: 10.1021/acs.jcim.3c00377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods for predicting ncRPIs have been developed, the problem of predicting ncRPIs remains challenging. It has always been the focus of ncRPIs research to select suitable feature extraction methods and develop a deep learning architecture with better recognition performance. In this work, we proposed an ensemble deep learning framework, RPI-EDLCN, based on a capsule network (CapsuleNet) to predict ncRPIs. In terms of feature input, we extracted the sequence features, secondary structure sequence features, motif information, and physicochemical properties of ncRNA/protein. The sequence and secondary structure sequence features of ncRNA/protein are encoded by the conjoint k-mer method and then input into an ensemble deep learning model based on CapsuleNet by combining the motif information and physicochemical properties. In this model, the encoding features are processed by convolution neural network (CNN), deep neural network (DNN), and stacked autoencoder (SAE). Then the advanced features obtained from the processing are input into the CapsuleNet for further feature learning. Compared with other state-of-the-art methods under 5-fold cross-validation, the performance of RPI-EDLCN is the best, and the accuracy of RPI-EDLCN on RPI1807, RPI2241, and NPInter v2.0 data sets was 93.8%, 88.2%, and 91.9%, respectively. The results of the independent test indicated that RPI-EDLCN can effectively predict potential ncRPIs in different organisms. In addition, RPI-EDLCN successfully predicted hub ncRNAs and proteins in Mus musculus ncRNA-protein networks. Overall, our model can be used as an effective tool to predict ncRPIs and provides some useful guidance for future biological studies.
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Affiliation(s)
- Xiaoyi Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
| | - Wenyan Qu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
| | - Jing Yan
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
| | - Jianjun Tan
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
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2
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Rocca R, Grillone K, Citriniti EL, Gualtieri G, Artese A, Tagliaferri P, Tassone P, Alcaro S. Targeting non-coding RNAs: Perspectives and challenges of in-silico approaches. Eur J Med Chem 2023; 261:115850. [PMID: 37839343 DOI: 10.1016/j.ejmech.2023.115850] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/08/2023] [Accepted: 09/29/2023] [Indexed: 10/17/2023]
Abstract
The growing information currently available on the central role of non-coding RNAs (ncRNAs) including microRNAs (miRNAS) and long non-coding RNAs (lncRNAs) for chronic and degenerative human diseases makes them attractive therapeutic targets. RNAs carry out different functional roles in human biology and are deeply deregulated in several diseases. So far, different attempts to therapeutically target the 3D RNA structures with small molecules have been reported. In this scenario, the development of computational tools suitable for describing RNA structures and their potential interactions with small molecules is gaining more and more interest. Here, we describe the most suitable strategies to study ncRNAs through computational tools. We focus on methods capable of predicting 2D and 3D ncRNA structures. Furthermore, we describe computational tools to identify, design and optimize small molecule ncRNA binders. This review aims to outline the state of the art and perspectives of computational methods for ncRNAs over the past decade.
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Affiliation(s)
- Roberta Rocca
- Department of Health Science, Magna Graecia University, Catanzaro, Italy; Net4Science srl, Academic Spinoff, Magna Græcia University, Catanzaro, Italy
| | - Katia Grillone
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | | | | | - Anna Artese
- Department of Health Science, Magna Graecia University, Catanzaro, Italy; Net4Science srl, Academic Spinoff, Magna Græcia University, Catanzaro, Italy.
| | | | - Pierfrancesco Tassone
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Stefano Alcaro
- Department of Health Science, Magna Graecia University, Catanzaro, Italy; Net4Science srl, Academic Spinoff, Magna Græcia University, Catanzaro, Italy
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3
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Navarro C, Salazar J, Díaz MP, Chacin M, Santeliz R, Vera I, D′Marco L, Parra H, Bernal MC, Castro A, Escalona D, García-Pacheco H, Bermúdez V. Intrinsic and environmental basis of aging: A narrative review. Heliyon 2023; 9:e18239. [PMID: 37576279 PMCID: PMC10415626 DOI: 10.1016/j.heliyon.2023.e18239] [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: 12/06/2022] [Revised: 06/26/2023] [Accepted: 07/12/2023] [Indexed: 08/15/2023] Open
Abstract
Longevity has been a topic of interest since the beginnings of humanity, yet its aetiology and precise mechanisms remain to be elucidated. Aging is currently viewed as a physiological phenomenon characterized by the gradual degeneration of organic physiology and morphology due to the passage of time where both external and internal stimuli intervene. The influence of intrinsic factors, such as progressive telomere shortening, genome instability due to mutation buildup, the direct or indirect actions of age-related genes, and marked changes in epigenetic, metabolic, and mitochondrial patterns constitute a big part of its underlying endogenous mechanisms. On the other hand, several psychosocial and demographic factors, such as diet, physical activity, smoking, and drinking habits, may have an even more significant impact on shaping the aging process. Consequentially, implementing dietary and exercise patterns has been proposed as the most viable alternative strategy for attenuating the most typical degenerative aging changes, thus increasing the likelihood of prolonging lifespan and achieving successful aging.
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Affiliation(s)
- Carla Navarro
- Endocrine and Metabolic Diseases Research Center. School of Medicine. University of Zulia. Maracaibo 4001, Venezuela
| | - Juan Salazar
- Endocrine and Metabolic Diseases Research Center. School of Medicine. University of Zulia. Maracaibo 4001, Venezuela
| | - María P. Díaz
- Endocrine and Metabolic Diseases Research Center. School of Medicine. University of Zulia. Maracaibo 4001, Venezuela
| | - Maricarmen Chacin
- Universidad Simón Bolívar, Facultad de Ciencias de la Salud, Barranquilla 080001, Colombia
| | - Raquel Santeliz
- Endocrine and Metabolic Diseases Research Center. School of Medicine. University of Zulia. Maracaibo 4001, Venezuela
| | - Ivana Vera
- Endocrine and Metabolic Diseases Research Center. School of Medicine. University of Zulia. Maracaibo 4001, Venezuela
| | - Luis D′Marco
- Universidad Cardenal Herrera-CEU Medicine Department, CEU Universities, 46115 Valencia, Spain
| | - Heliana Parra
- Endocrine and Metabolic Diseases Research Center. School of Medicine. University of Zulia. Maracaibo 4001, Venezuela
| | | | - Ana Castro
- Endocrine and Metabolic Diseases Research Center. School of Medicine. University of Zulia. Maracaibo 4001, Venezuela
| | - Daniel Escalona
- Endocrine and Metabolic Diseases Research Center. School of Medicine. University of Zulia. Maracaibo 4001, Venezuela
| | - Henry García-Pacheco
- Universidad del Zulia, Facultad de Medicina, Departamento de Cirugía. Hospital General del Sur “Dr. Pedro Iturbe”. Maracaibo, Venezuela
- Unidad de Cirugía para la Obesidad y Metabolismo (UCOM). Maracaibo, Venezuela
| | - Valmore Bermúdez
- Universidad Simón Bolívar, Facultad de Ciencias de la Salud, Barranquilla 080001, Colombia
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4
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Pepe G, Appierdo R, Carrino C, Ballesio F, Helmer-Citterich M, Gherardini PF. Artificial intelligence methods enhance the discovery of RNA interactions. Front Mol Biosci 2022; 9:1000205. [PMID: 36275611 PMCID: PMC9585310 DOI: 10.3389/fmolb.2022.1000205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Understanding how RNAs interact with proteins, RNAs, or other molecules remains a challenge of main interest in biology, given the importance of these complexes in both normal and pathological cellular processes. Since experimental datasets are starting to be available for hundreds of functional interactions between RNAs and other biomolecules, several machine learning and deep learning algorithms have been proposed for predicting RNA-RNA or RNA-protein interactions. However, most of these approaches were evaluated on a single dataset, making performance comparisons difficult. With this review, we aim to summarize recent computational methods, developed in this broad research area, highlighting feature encoding and machine learning strategies adopted. Given the magnitude of the effect that dataset size and quality have on performance, we explored the characteristics of these datasets. Additionally, we discuss multiple approaches to generate datasets of negative examples for training. Finally, we describe the best-performing methods to predict interactions between proteins and specific classes of RNA molecules, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), and methods to predict RNA-RNA or RNA-RBP interactions independently of the RNA type.
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Affiliation(s)
- G Pepe
- Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
- *Correspondence: G Pepe, ; M Helmer-Citterich,
| | - R Appierdo
- Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
| | - C Carrino
- PhD Program in Cellular and Molecular Biology, Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
| | - F Ballesio
- PhD Program in Cellular and Molecular Biology, Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
| | - M Helmer-Citterich
- Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
- *Correspondence: G Pepe, ; M Helmer-Citterich,
| | - PF Gherardini
- Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
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5
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Wang Z, Dai Q, Song J, Duan X, Yang H, Yang Z. Predicting RBP Binding Sites of RNA With High-Order Encoding Features and CNN-BLSTM Hybrid Model. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2409-2419. [PMID: 34038367 DOI: 10.1109/tcbb.2021.3083930] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
RNA binding protein (RBP) is extensively involved in various cellular regulatory processes through the interaction with RNAs. Capturing the RBP binding preferences is fundamental for revealing the pathogenesis of complex diseases. Many experimental detection techniques are still time-consuming and labor-intensive, therefore, it is indispensable to develop a computational method with convincing accuracy. In this study, we proposed a CNN-BLSTM hybrid deep learning framework, named DeepDW, for predicting the RBP binding sites on RNAs with high-order encoding features of RNA sequence and secondary structure. The high-order encoding strategy was used to characterize the dependencies among adjacency nucleotides. For CNN-BLSTM hybrid model, DeepDW first employed two 1-D convolutional neural networks (CNNs) for learning the local features from high-order encoded matrices of RNA sequence and structure separately, and then applied two bidirectional long short-term memory networks (BLSTMs) to capture the global information in a higher level. Moreover, a series of experiments were carried out on 31 public datasets to evaluate our proposed framework, and DeepDW achieved superior performance than the state-of-the-art methods. The results indicated that the combination of high-order encoding method and CNN-BLSTM hybrid model had advantages in identifying RBP-RNA binding sites.
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6
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Xu D, Yuan W, Fan C, Liu B, Lu MZ, Zhang J. Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants. FRONTIERS IN PLANT SCIENCE 2022; 13:890663. [PMID: 35498708 PMCID: PMC9048598 DOI: 10.3389/fpls.2022.890663] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 03/28/2022] [Indexed: 06/01/2023]
Affiliation(s)
- Dong Xu
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenya Yuan
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
| | - Chunjie Fan
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, China
| | - Bobin Liu
- Jiangsu Key Laboratory for Bioresources of Saline Soils, Jiangsu Synthetic Innovation Center for Coastal Bio-agriculture, School of Wetlands, Yancheng Teachers University, Yancheng, China
| | - Meng-Zhu Lu
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
| | - Jin Zhang
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
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7
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Kang Q, Meng J, Luan Y. RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction. Brief Bioinform 2022; 23:6555402. [PMID: 35352114 DOI: 10.1093/bib/bbac107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/22/2022] [Accepted: 03/02/2022] [Indexed: 11/12/2022] Open
Abstract
Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experiments can confirm RNA-RNA interactions to facilitate the exploration of their biological functions, but they are expensive and time-consuming. Machine learning models can predict potential RNA-RNA interactions, which provide candidates for molecular biology experiments to save a lot of time and cost. Using a set of suitable features to represent the sample is crucial for training powerful models, but there is a lack of effective feature representation for RNA-RNA interaction. This study proposes a novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction (named RNAI-FRID). Diverse base features are first extracted from RNA data to contain more sample information. Then, the extracted base features are used to construct the complex features through an arithmetic-level method. It greatly reduces the feature dimension while keeping the relationship between molecule features. Since the dimension reduction may cause information loss, in the process of complex feature construction, the arithmetic mean strategy is adopted to enhance the sample information further. Finally, three feature ranking methods are integrated for feature selection on constructed complex features. It can adaptively retain important features and remove redundant ones. Extensive experiment results show that RNAI-FRID can provide reliable feature representation for RNA-RNA interaction with higher efficiency and the model trained with generated features obtain better performance than other deep neural network predictors.
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Affiliation(s)
- Qiang Kang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, China
| | - Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, China
| | - Yushi Luan
- School of Bioengineering, Dalian University of Technology, Dalian, Liaoning, 116024, China
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8
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Yu B, Wang X, Zhang Y, Gao H, Wang Y, Liu Y, Gao X. RPI-MDLStack: Predicting RNA-protein interactions through deep learning with stacking strategy and LASSO. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108676] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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9
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Rincón-Riveros A, Morales D, Rodríguez JA, Villegas VE, López-Kleine L. Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions. Int J Mol Sci 2021; 22:11397. [PMID: 34768830 PMCID: PMC8583695 DOI: 10.3390/ijms222111397] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 12/16/2022] Open
Abstract
Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.
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Affiliation(s)
- Andrés Rincón-Riveros
- Bioinformatics and Systems Biology Group, Universidad Nacional de Colombia, Bogotá 111221, Colombia;
| | - Duvan Morales
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá 111221, Colombia;
| | - Josefa Antonia Rodríguez
- Grupo de Investigación en Biología del Cáncer, Instituto Nacional de Cancerología, Bogotá 111221, Colombia;
| | - Victoria E. Villegas
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá 111221, Colombia;
| | - Liliana López-Kleine
- Department of Statistics, Faculty of Science, Universidad Nacional de Colombia, Bogotá 111221, Colombia
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10
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Kang Q, Meng J, Su C, Luan Y. Mining plant endogenous target mimics from miRNA-lncRNA interactions based on dual-path parallel ensemble pruning method. Brief Bioinform 2021; 23:6399881. [PMID: 34662389 DOI: 10.1093/bib/bbab440] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/07/2021] [Accepted: 09/24/2021] [Indexed: 12/14/2022] Open
Abstract
The interactions between microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) play important roles in biological activities. Specially, lncRNAs as endogenous target mimics (eTMs) can bind miRNAs to regulate the expressions of target messenger RNAs (mRNAs). A growing number of studies focus on animals, but the studies on plants are scarce and many functions of plant eTMs are unknown. This study proposes a novel ensemble pruning protocol for predicting plant miRNA-lncRNA interactions at first. It adaptively prunes the base models based on dual-path parallel ensemble method to meet the challenge of cross-species prediction. Then potential eTMs are mined from predicted results. The expression levels of RNAs are identified through biological experiment to construct the lncRNA-miRNA-mRNA regulatory network, and the functions of potential eTMs are inferred through enrichment analysis. Experiment results show that the proposed protocol outperforms existing methods and state-of-the-art predictors on various plant species. A total of 17 potential eTMs are verified by biological experiment to involve in 22 regulations, and 14 potential eTMs are inferred by Gene Ontology enrichment analysis to involve in 63 functions, which is significant for further research.
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Affiliation(s)
- Qiang Kang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Chenglin Su
- School of Bioengineering, Dalian University of Technology, Dalian, Liaoning, 116024 China
| | - Yushi Luan
- School of Bioengineering, Dalian University of Technology, Dalian, Liaoning, 116024 China
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11
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Kang Q, Meng J, Shi W, Luan Y. Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA-lncRNA Interaction Prediction. Interdiscip Sci 2021; 13:603-614. [PMID: 33900552 DOI: 10.1007/s12539-021-00434-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/01/2021] [Accepted: 04/16/2021] [Indexed: 12/18/2022]
Abstract
MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are both non-coding RNAs (ncRNAs) and their interactions play important roles in biological processes. Computational methods, such as machine learning and various bioinformatics tools, can predict potential miRNA-lncRNA interactions, which is significant for studying their mechanisms and biological functions. A growing number of RNA interaction predictors for animal have been reported, but they are unreliable for plant due to the differences of ncRNAs in animal and plant. It is urgent to build a reliable plant predictor, especially for cross-species. This paper proposes an ensemble deep learning model based on multi-level information enhancement and greedy fuzzy decision (PmliPEMG) for plant miRNA-lncRNA interaction prediction. The fusion complex features, multi-scale convolutional long short-term memory networks, and attention mechanism are adopted to enhance the sample information at the feature, scale, and model levels, respectively. An ensemble deep learning model is built based on a novel method (greedy fuzzy decision) which greatly improves the efficiency. The multi-level information enhancement and greedy fuzzy decision are verified to have the positive effects on prediction performance. PmliPEMG can be applied to the cross-species prediction. It shows better performance and stronger generalization ability than state-of-the-art predictors and may provide valuable references for related research.
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Affiliation(s)
- Qiang Kang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China
| | - Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.
| | - Wenhao Shi
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China
| | - Yushi Luan
- School of Bioengineering, Dalian University of Technology, Dalian, 116024, Liaoning, China
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12
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Wang J, Zhao Y, Gong W, Liu Y, Wang M, Huang X, Tan J. EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction. BMC Bioinformatics 2021; 22:133. [PMID: 33740884 PMCID: PMC7980572 DOI: 10.1186/s12859-021-04069-9] [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: 01/23/2021] [Accepted: 03/05/2021] [Indexed: 11/29/2022] Open
Abstract
Background Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA–protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA–protein interactions. Results In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA–protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA–protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA–protein networks of Mus musculus successfully. Conclusions In general, our proposed method EDLMFC improved the accuracy of ncRNA–protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04069-9.
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Affiliation(s)
- Jingjing Wang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing, 100124, China
| | - Yanpeng Zhao
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing, 100124, China
| | - Weikang Gong
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing, 100124, China
| | - Yang Liu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing, 100124, China
| | - Mei Wang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing, 100124, China
| | - Xiaoqian Huang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing, 100124, China
| | - Jianjun Tan
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing, 100124, China.
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13
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Zhang SW, Zhang XX, Fan XN, Li WN. LPI-CNNCP: Prediction of lncRNA-protein interactions by using convolutional neural network with the copy-padding trick. Anal Biochem 2020; 601:113767. [PMID: 32454029 DOI: 10.1016/j.ab.2020.113767] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 04/27/2020] [Accepted: 05/01/2020] [Indexed: 11/17/2022]
Abstract
Long noncoding RNAs (lncRNAs) play critical roles in many pathological and biological processes, such as post-transcription, cell differentiation and gene regulation. Increasingly more studies have shown that lncRNAs function through mainly interactions with specific RNA binding proteins (RBPs). However, experimental identification of potential lncRNA-protein interactions is costly and time-consuming. In this work, we propose a novel convolutional neural network-based method with the copy-padding trick (named LPI-CNNCP) to predict lncRNA-protein interactions. The copy-padding trick of the LPI-CNNCP convert the protein/RNA sequences with variable-length into the fixed-length sequences, thus enabling the construction of the CNN model. A high-order one-hot encoding is also applied to transform the protein/RNA sequences into image-like inputs for capturing the dependencies among amino acids (or nucleotides). In the end, these encoded protein/RNA sequences are feed into a CNN to predict the lncRNA-protein interactions. Compared with other state-of-the-art methods in 10-fold cross-validation (10CV) test, LPI-CNNCP shows the best performance. Results in the independent test demonstrate that our LPI-CNNCP can effectively predict the potential lncRNA-protein interactions. We also compared the copy-padding trick with two other existing tricks (i.e., zero-padding and cropping), and the results show that our copy-padding rick outperforms the zero-padding and cropping tricks on predicting lncRNA-protein interactions. The source code of LPI-CNNCP and the datasets used in this work are available at https://github.com/NWPU-903PR/LPI-CNNCP for academic users.
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Affiliation(s)
- Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Xi-Xi Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Xiao-Nan Fan
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Wei-Na Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
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14
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Wang Z, Lei X, Wu FX. Identifying Cancer-Specific circRNA-RBP Binding Sites Based on Deep Learning. Molecules 2019; 24:E4035. [PMID: 31703384 PMCID: PMC6891306 DOI: 10.3390/molecules24224035] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 10/25/2019] [Accepted: 11/06/2019] [Indexed: 12/17/2022] Open
Abstract
Circular RNAs (circRNAs) are extensively expressed in cells and tissues, and play crucial roles in human diseases and biological processes. Recent studies have reported that circRNAs could function as RNA binding protein (RBP) sponges, meanwhile RBPs can also be involved in back-splicing. The interaction with RBPs is also considered an important factor for investigating the function of circRNAs. Hence, it is necessary to understand the interaction mechanisms of circRNAs and RBPs, especially in human cancers. Here, we present a novel method based on deep learning to identify cancer-specific circRNA-RBP binding sites (CSCRSites), only using the nucleotide sequences as the input. In CSCRSites, an architecture with multiple convolution layers is utilized to detect the features of the raw circRNA sequence fragments, and further identify the binding sites through a fully connected layer with the softmax output. The experimental results show that CSCRSites outperform the conventional machine learning classifiers and some representative deep learning methods on the benchmark data. In addition, the features learnt by CSCRSites are converted to sequence motifs, some of which can match to human known RNA motifs involved in human diseases, especially cancer. Therefore, as a deep learning-based tool, CSCRSites could significantly contribute to the function analysis of cancer-associated circRNAs.
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Affiliation(s)
- Zhengfeng Wang
- School of Computer Science, Shaanxi Normal University, Xi’an 710119, China;
- College of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi’an 710119, China;
| | - Fang-Xiang Wu
- Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada;
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15
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Pan X, Yang Y, Xia C, Mirza AH, Shen H. Recent methodology progress of deep learning for RNA–protein interaction prediction. WILEY INTERDISCIPLINARY REVIEWS-RNA 2019; 10:e1544. [DOI: 10.1002/wrna.1544] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 04/07/2019] [Accepted: 04/11/2019] [Indexed: 12/17/2022]
Affiliation(s)
- Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China
- IDLab, Department for Electronics and Information Systems Ghent University Ghent Belgium
- BASF Agriculture Solution Ghent Belgium
| | - Yang Yang
- Department of Computer Science Shanghai Jiao Tong University, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China
| | - Chun‐Qiu Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China
| | - Aashiq H. Mirza
- Department of Pharmacology Weill Cornell Medicine New York New York
| | - Hong‐Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China
- Department of Computer Science Shanghai Jiao Tong University, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China
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