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Chen X, Huang L. Computational model for drug research. Brief Bioinform 2024; 25:bbae158. [PMID: 38581423 PMCID: PMC10998638 DOI: 10.1093/bib/bbae158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 04/08/2024] Open
Abstract
This special issue focuses on computational model for drug research regarding drug bioactivity prediction, drug-related interaction prediction, modelling for immunotherapy and modelling for treatment of a specific disease, as conveyed by the following six research and four review articles. Notably, these 10 papers described a wide variety of in-depth drug research from the computational perspective and may represent a snapshot of the wide research landscape.
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Affiliation(s)
- Xing Chen
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Li Huang
- The Future Laboratory, Tsinghua University, Beijing, 100084, China
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2
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Huang H, Huang H, Xia Z, Yang Y, Jiang X, Huang C, Yang Y, Wang D, Chen Z. Sequencing, Functional Annotation, and Interaction Prediction of mRNAs and Candidate Long Noncoding RNAs Originating from Tea Leaves During Infection by the Fungal Pathogen Causing Tea Leaf Spot, Didymella bellidis. Plant Dis 2023; 107:2830-2834. [PMID: 37707825 DOI: 10.1094/pdis-05-22-1240-a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
Tea leaf spot caused by Didymella bellidis can seriously reduce the productivity and quality of tea (Camellia sinensis var. sinensis) leaves in Guizhou Province, southwest China. Analysis of the relationship between messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) of tea could provide insights into the plant-pathogen interaction. In this study, high-throughput sequencing of mRNAs and lncRNAs from tea leaves during infection by D. bellidis was conducted using the Illumina Novaseq 6000 platform. Infection by D. bellidis hyphae resulted in up- or downregulation of 553 and 191 of the differentially expressed mRNAs (DEmRNAs), respectively. As the S gene number (total number of genes with significantly differential expression annotated in the specified Gene Ontology [GO] database), three were enriched with respect to the defense response to the fungus at the biological process level. Expression of the DEmRNAs peroxidase 21 (TEA000222.1) and mcht-2 (TEA013240.1) originating from tea leaves were upregulated during challenge by D. bellidis hyphae, whereas expression of the LRR receptor-like serine/threonine-protein kinase ERECTA (TEA016781.1) gene was downregulated. The infection of D. bellidis hyphae resulted in up- or downregulation of 227 and 958 of the differentially expressed lncRNAs (DElncRNAs). The DEmRNAs associated with uncharacterized LOC101499401 (TEA015626.1), uncharacterized protein (TEA014125.1), structural maintenance of chromosomes protein 1 (TEA001660.1), and uncharacterized protein (TEA017727.1) occurred as a result of cis regulation by DElncRNAs MSTRG.20036, MSTRG.3843, MSTRG.26132, and MSTRG.56701, respectively. The expression profiling and lncRNA/mRNA association prediction in the tea leaves infected by D. bellidis will provide a valuable resource for further research into disease resistance.
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Affiliation(s)
- Honglin Huang
- Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China
| | - Hongke Huang
- Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China
- College of Tea Science, Guizhou University, Guiyang, Guizhou 550025, China
| | - Zhongqiu Xia
- Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China
- College of Tea Science, Guizhou University, Guiyang, Guizhou 550025, China
| | - Yuqin Yang
- Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China
- College of Tea Science, Guizhou University, Guiyang, Guizhou 550025, China
| | - Xinyue Jiang
- Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China
| | - Chen Huang
- Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China
| | - Yuanyou Yang
- Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China
| | - Delu Wang
- College of Forestry, Guizhou University, Guiyang, Guizhou 550025, China
| | - Zhuo Chen
- Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China
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3
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Velásquez-Zapata V, Elmore JM, Wise RP. Bioinformatic Analysis of Yeast Two-Hybrid Next-Generation Interaction Screen Data. Methods Mol Biol 2023; 2690:223-239. [PMID: 37450151 DOI: 10.1007/978-1-0716-3327-4_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Yeast two-hybrid next-generation interaction screening (Y2H-NGIS) uses the output of next-generation sequencing to mine for novel protein-protein interactions. Here, we outline the analytics underlying Y2H-NGIS datasets. Different systems, libraries, and experimental designs comprise Y2H-NGIS methodologies. We summarize the analysis in several layers that comprise the characterization of baits and preys, quantification, and identification of true interactions for subsequent secondary validation. We present two software designed for this purpose, NGPINT and Y2H-SCORES, which are used as front-end and back-end tools in the analysis. Y2H-SCORES software can be used and adapted to analyze different datasets not only from Y2H-NGIS but from other techniques ruled by similar biological principles.
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Affiliation(s)
- Valeria Velásquez-Zapata
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA, USA.
- Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, USA.
| | - J Mitch Elmore
- Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, USA
- USDA-Agricultural Research Service, Cereal Disease Laboratory, St. Paul, MN, USA
- USDA-Agricultural Research Service, Corn Insects and Crop Genetics Research, Ames, IA, USA
| | - Roger P Wise
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA, USA.
- Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, USA.
- USDA-Agricultural Research Service, Corn Insects and Crop Genetics Research, Ames, IA, USA.
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4
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Han S, Yang X, Sun H, Yang H, Zhang Q, Peng C, Fang W, Li Y. LION: an integrated R package for effective prediction of ncRNA-protein interaction. Brief Bioinform 2022; 23:6713512. [PMID: 36155620 DOI: 10.1093/bib/bbac420] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/03/2022] [Accepted: 08/30/2022] [Indexed: 12/14/2022] Open
Abstract
Understanding ncRNA-protein interaction is of critical importance to unveil ncRNAs' functions. Here, we propose an integrated package LION which comprises a new method for predicting ncRNA/lncRNA-protein interaction as well as a comprehensive strategy to meet the requirement of customisable prediction. Experimental results demonstrate that our method outperforms its competitors on multiple benchmark datasets. LION can also improve the performance of some widely used tools and build adaptable models for species- and tissue-specific prediction. We expect that LION will be a powerful and efficient tool for the prediction and analysis of ncRNA/lncRNA-protein interaction. The R Package LION is available on GitHub at https://github.com/HAN-Siyu/LION/.
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Affiliation(s)
- Siyu Han
- College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, in Jilin University, China
| | - Xiao Yang
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Hang Sun
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Hu Yang
- 964 Hospital of Joint Logistic Support Force of the Chinese People's Liberation Army
| | - Qi Zhang
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Cheng Peng
- School of Software, Tsinghua University, Beijing, China
| | - Wensi Fang
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Ying Li
- College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
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5
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Kc K, Li R, Cui F, Haake AR. Predicting Biomedical Interactions With Higher-Order Graph Convolutional Networks. IEEE/ACM Trans Comput Biol Bioinform 2022; 19:676-687. [PMID: 33587705 PMCID: PMC8518029 DOI: 10.1109/tcbb.2021.3059415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Biomedical interaction networks have incredible potential to be useful in the prediction of biologically meaningful interactions, identification of network biomarkers of disease, and the discovery of putative drug targets. Recently, graph neural networks have been proposed to effectively learn representations for biomedical entities and achieved state-of-the-art results in biomedical interaction prediction. These methods only consider information from immediate neighbors but cannot learn a general mixing of features from neighbors at various distances. In this paper, we present a higher-order graph convolutional network (HOGCN)to aggregate information from the higher-order neighborhood for biomedical interaction prediction. Specifically, HOGCN collects feature representations of neighbors at various distances and learns their linear mixing to obtain informative representations of biomedical entities. Experiments on four interaction networks, including protein-protein, drug-drug, drug-target, and gene-disease interactions, show that HOGCN achieves more accurate and calibrated predictions. HOGCN performs well on noisy, sparse interaction networks when feature representations of neighbors at various distances are considered. Moreover, a set of novel interaction predictions are validated by literature-based case studies.
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Yakimovich A, Özgür A, Doğan T, Ozkirimli E. Editorial: Machine Learning Methodologies to Study Molecular Interactions. Front Mol Biosci 2021; 8:806474. [PMID: 34926587 PMCID: PMC8678493 DOI: 10.3389/fmolb.2021.806474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 11/10/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Artur Yakimovich
- Pharma International Informatics, Roche Products Limited, Welwyn Garden City, United Kingdom
| | - Arzucan Özgür
- Department of Computer Engineering, Bogazici University, Istanbul, Turkey
| | - Tunca Doğan
- Department of Computer Engineering, Hacettepe University, Ankara, Turkey
| | - Elif Ozkirimli
- Pharma International Informatics, F. Hoffmann-La Roche AG, Basel, Switzerland
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Du H, Chen F, Liu H, Hong P. Network-based virus-host interaction prediction with application to SARS-CoV-2. Patterns (N Y) 2021; 2:100242. [PMID: 33817672 PMCID: PMC8006187 DOI: 10.1016/j.patter.2021.100242] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/06/2021] [Accepted: 03/24/2021] [Indexed: 12/15/2022]
Abstract
COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has quickly become a global health crisis since the first report of infection in December of 2019. However, the infection spectrum of SARS-CoV-2 and its comprehensive protein-level interactions with hosts remain unclear. There is a massive amount of underutilized data and knowledge about RNA viruses highly relevant to SARS-CoV-2 and proteins of their hosts. More in-depth and more comprehensive analyses of that knowledge and data can shed new light on the molecular mechanisms underlying the COVID-19 pandemic and reveal potential risks. In this work, we constructed a multi-layer virus-host interaction network to incorporate these data and knowledge. We developed a machine-learning-based method to predict virus-host interactions at both protein and organism levels. Our approach revealed five potential infection targets of SARS-CoV-2 and 19 highly possible interactions between SARS-CoV-2 proteins and human proteins in the innate immune pathway.
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Affiliation(s)
- Hangyu Du
- Department of Computer Science, Brandeis University, Waltham, MA 02453, USA
| | - Feng Chen
- Department of Computer Science, Brandeis University, Waltham, MA 02453, USA
| | - Hongfu Liu
- Department of Computer Science, Brandeis University, Waltham, MA 02453, USA
| | - Pengyu Hong
- Department of Computer Science, Brandeis University, Waltham, MA 02453, USA
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8
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Shi Q, Chen W, Huang S, Wang Y, Xue Z. Deep learning for mining protein data. Brief Bioinform 2019; 22:194-218. [PMID: 31867611 DOI: 10.1093/bib/bbz156] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 10/21/2019] [Accepted: 11/07/2019] [Indexed: 01/16/2023] Open
Abstract
The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.
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Affiliation(s)
- Qiang Shi
- School of Software Engineering, Huazhong University of Science and Technology. His main interests cover machine learning especially deep learning, protein data analysis, and big data mining
| | - Weiya Chen
- School of Software Engineering, Huazhong University of Science & Technology, Wuhan, China. His research interests cover bioinformatics, virtual reality, and data visualization
| | - Siqi Huang
- Software Engineering at Huazhong University of science and technology, focusing on Machine learning and data mining
| | - Yan Wang
- School of life, University of Science & Technology; her main interests cover protein structure and function prediction and big data mining
| | - Zhidong Xue
- School of Software Engineering, Huazhong University of Science & Technology, Wuhan, China. His research interests cover bioinformatics, machine learning, and image processing
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9
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Xie G, Wu C, Sun Y, Fan Z, Liu J. LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm. Front Genet 2019; 10:343. [PMID: 31057602 PMCID: PMC6482170 DOI: 10.3389/fgene.2019.00343] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Accepted: 03/29/2019] [Indexed: 12/26/2022] Open
Abstract
According to the latest research, lncRNAs (long non-coding RNAs) play a broad and important role in various biological processes by interacting with proteins. However, identifying whether proteins interact with a specific lncRNA through biological experimental methods is difficult, costly, and time-consuming. Thus, many bioinformatics computational methods have been proposed to predict lncRNA-protein interactions. In this paper, we proposed a novel approach called Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA). In the proposed method, we implemented a two-round resource allocation and eliminated the second-order correlations appropriately on the bipartite network. Experimental results illustrate that LPI-IBNRA outperforms five previous methods, with the AUC values of 0.8932 in leave-one-out cross validation (LOOCV) and 0.8819 ± 0.0052 in 10-fold cross validation, respectively. In addition, case studies on four lncRNAs were carried out to show the predictive power of LPI-IBNRA.
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Affiliation(s)
- Guobo Xie
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Cuiming Wu
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Yuping Sun
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Zhiliang Fan
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Jianghui Liu
- Department of Emergency, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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10
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Zhao Q, Zhang Y, Hu H, Ren G, Zhang W, Liu H. IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction. Front Genet 2018; 9:239. [PMID: 30023002 PMCID: PMC6040094 DOI: 10.3389/fgene.2018.00239] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 06/15/2018] [Indexed: 11/13/2022] Open
Abstract
Long non-coding RNA (lncRNA) plays an important role in many important biological processes and has attracted widespread attention. Although the precise functions and mechanisms for most lncRNAs are still unknown, we are certain that lncRNAs usually perform their functions by interacting with the corresponding RNA- binding proteins. For example, lncRNA-protein interactions play an important role in post transcriptional gene regulation, such as splicing, translation, signaling, and advances in complex diseases. However, experimental verification of lncRNA-protein interactions prediction is time-consuming and laborious. In this work, we propose a computational method, named IRWNRLPI, to find the potential associations between lncRNAs and proteins. IRWNRLPI integrates two algorithms, random walk and neighborhood regularized logistic matrix factorization, which can optimize a lot more than using an algorithm alone. Moreover, the method is semi-supervised and does not require negative samples. Based on the leave-one-out cross validation, we obtain the AUC of 0.9150 and the AUPR of 0.7138, demonstrating its reliable performance. In addition, by means of case study in the “Mus musculus,” many lncRNA-protein interactions which are predicted by our method can be successfully confirmed by experiments. This suggests that IRWNRLPI will be a useful bioinformatics resource in biomedical research.
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Affiliation(s)
- Qi Zhao
- School of Mathematics, Liaoning University, Shenyang, China.,Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, China
| | - Yue Zhang
- School of Mathematics, Liaoning University, Shenyang, China
| | - Huan Hu
- School of Life Science, Liaoning University, Shenyang, China
| | - Guofei Ren
- School of Information, Liaoning University, Shenyang, China
| | - Wen Zhang
- School of Computer, Wuhan University, Wuhan, China
| | - Hongsheng Liu
- Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, China.,School of Life Science, Liaoning University, Shenyang, China.,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, China
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11
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Liu H, Ren G, Hu H, Zhang L, Ai H, Zhang W, Zhao Q. LPI-NRLMF: lncRNA-protein interaction prediction by neighborhood regularized logistic matrix factorization. Oncotarget 2017; 8:103975-103984. [PMID: 29262614 PMCID: PMC5732780 DOI: 10.18632/oncotarget.21934] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 08/28/2017] [Indexed: 01/08/2023] Open
Abstract
LncRNA-protein interactions play important roles in many important cellular processes including signaling, transcriptional regulation, and even the generation and progression of complex diseases. However, experimental methods for determining proteins bound by a specific lncRNA remain expensive, difficult and time-consuming, and only a few theoretical approaches are available for predicting potential lncRNA-protein associations. In this study, we developed a novel matrix factorization computational approach to uncover lncRNA-protein relationships, namely lncRNA-protein interactions prediction by neighborhood regularized logistic matrix factorization (LPI-NRLMF). Moreover, it is a semi-supervised and does not need negative samples. As a result, new model obtained reliable performance in the leave-one-out cross validation (the AUC of 0.9025 and AUPR of 0.6924), which significantly improved the prediction performance of previous models. Furthermore, the case study demonstrated that many lncRNA-protein interactions predicted by our method can be successfully confirmed by experiments. It is anticipated that LPI-NRLMF could serve as a useful resource for potential lncRNA-protein association identification.
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Affiliation(s)
- Hongsheng Liu
- School of Life Science, Liaoning University, Shenyang, 110036, China.,Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China.,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China
| | - Guofei Ren
- School of Information, Liaoning University, Shenyang, 110036, China
| | - Huan Hu
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Li Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Haixin Ai
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Wen Zhang
- School of Computer, Wuhan University, Wuhan, 430072, China
| | - Qi Zhao
- Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China.,School of Mathematics, Liaoning University, Shenyang, 110036, China
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12
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Chang JW, Zhou YQ, Ul Qamar MT, Chen LL, Ding YD. Prediction of Protein-Protein Interactions by Evidence Combining Methods. Int J Mol Sci 2016; 17:ijms17111946. [PMID: 27879651 PMCID: PMC5133940 DOI: 10.3390/ijms17111946] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 11/15/2016] [Accepted: 11/15/2016] [Indexed: 12/27/2022] Open
Abstract
Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.
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Affiliation(s)
- Ji-Wei Chang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Yan-Qing Zhou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Muhammad Tahir Ul Qamar
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Ling-Ling Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Yu-Duan Ding
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
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13
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Abstract
High-throughput methods for screening of physical and functional interactions now provide the means to study virus-host interactions on a genome scale. The limited coverage of these methods and the large size and uncertain quality of the identified interaction sets, however, require sophisticated computational approaches to obtain novel insights and hypotheses on virus infection processes from these interactions. Here, we describe the central steps of bioinformatics methods applied most commonly for this task and highlight important aspects that need to be considered and potential pitfalls that should be avoided.
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Affiliation(s)
- Susanne M. Bailer
- University of Stuttgart Institute of Interfacial Process, Stuttgart, Germany
| | - Diana Lieber
- Ulm University Medical Center Institute of Virology, Ulm, Germany
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14
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Arnold R, Boonen K, Sun MG, Kim PM. Computational analysis of interactomes: current and future perspectives for bioinformatics approaches to model the host-pathogen interaction space. Methods 2012; 57:508-18. [PMID: 22750305 PMCID: PMC7128575 DOI: 10.1016/j.ymeth.2012.06.011] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2012] [Revised: 06/20/2012] [Accepted: 06/21/2012] [Indexed: 11/05/2022] Open
Abstract
Bacterial and viral pathogens affect their eukaryotic host partly by interacting with proteins of the host cell. Hence, to investigate infection from a systems' perspective we need to construct complete and accurate host-pathogen protein-protein interaction networks. Because of the paucity of available data and the cost associated with experimental approaches, any construction and analysis of such a network in the near future has to rely on computational predictions. Specifically, this challenge consists of a number of sub-problems: First, prediction of possible pathogen interactors (e.g. effector proteins) is necessary for bacteria and protozoa. Second, the prospective host binding partners have to be determined and finally, the impact on the host cell analyzed. This review gives an overview of current bioinformatics approaches to obtain and understand host-pathogen interactions. As an application example of the methods covered, we predict host-pathogen interactions of Salmonella and discuss the value of these predictions as a prospective for further research.
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Affiliation(s)
- Roland Arnold
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1
| | - Kurt Boonen
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1
| | - Mark G.F. Sun
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1
| | - Philip M. Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1
- Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada M5S 3E1
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 3E1
- Department of Computer Science, University of Toronto, Toronto, ON, Canada M5S 3E1
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