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Giordano M, Falbo E, Maddalena L, Piccirillo M, Granata I. Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience. Biomolecules 2023; 14:18. [PMID: 38254618 PMCID: PMC10813179 DOI: 10.3390/biom14010018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/29/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this information has been used to reduce the genomes of model organisms. A growing amount of evidence highlights that essentiality is a property that depends on the context. Because of their importance in vital biological processes, recognising context-specific EGs (csEGs) could help for identifying new potential pharmacological targets and to improve precision therapeutics. Since most of the computational procedures proposed to identify and predict EGs neglect their context-specificity, we focused on this aspect, providing a theoretical and experimental overview of the literature, data and computational methods dedicated to recognising csEGs. To this end, we adapted existing computational methods to exploit a specific context (the kidney tissue) and experimented with four different prediction methods using the labels provided by four different identification approaches. The considerations derived from the analysis of the obtained results, confirmed and validated also by further experiments for a different tissue context, provide the reader with guidance on exploiting existing tools for achieving csEGs identification and prediction.
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Affiliation(s)
- Maurizio Giordano
- Institute for High-Performance Computing and Networking (ICAR), National Research Council (CNR), V. Pietro Castellino 111, 80131 Naples, Italy; (E.F.); (L.M.); (M.P.); (I.G.)
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2
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Sun J, Pan L, Li B, Wang H, Yang B, Li W. A Construction Method of Dynamic Protein Interaction Networks by Using Relevant Features of Gene Expression Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2790-2801. [PMID: 37030714 DOI: 10.1109/tcbb.2023.3264241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Essential proteins play an important role in various life activities and are considered to be a vital part of the organism. Gene expression data are an important dataset to construct dynamic protein-protein interaction networks (DPIN). The existing methods for the construction of DPINs generally utilize all features (or the features in a cycle) of the gene expression data. However, the features observed from successive time points tend to be highly correlated, and thus there are some redundant and irrelevant features in the gene expression data, which will influence the quality of the constructed network and the predictive performance of essential proteins. To address this problem, we propose a construction method of DPINs by using selected relevant features rather than continuous and periodic features. We adopt an improved unsupervised feature selection method based on Laplacian algorithm to remove irrelevant and redundant features from gene expression data, then integrate the chosen relevant features into the static protein-protein interaction network (SPIN) to construct a more concise and effective DPIN (FS-DPIN). To evaluate the effectiveness of the FS-DPIN, we apply 15 network-based centrality methods on the FS-DPIN and compare the results with those on the SPIN and the existing DPINs. Then the predictive performance of the 15 centrality methods is validated in terms of sensitivity, specificity, positive predictive value, negative predictive value, F-measure, accuracy, Jackknife and AUPRC. The experimental results show that the FS-DPIN is superior to the existing DPINs in the identification accuracy of essential proteins.
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3
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Chen S, Huang C, Wang L, Zhou S. A disease-related essential protein prediction model based on the transfer neural network. Front Genet 2023; 13:1087294. [PMID: 36685976 PMCID: PMC9845409 DOI: 10.3389/fgene.2022.1087294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/14/2022] [Indexed: 01/06/2023] Open
Abstract
Essential proteins play important roles in the development and survival of organisms whose mutations are proven to be the drivers of common internal diseases having higher prevalence rates. Due to high costs of traditional biological experiments, an improved Transfer Neural Network (TNN) was designed to extract raw features from multiple biological information of proteins first, and then, based on the newly-constructed Transfer Neural Network, a novel computational model called TNNM was designed to infer essential proteins in this paper. Different from traditional Markov chain, since Transfer Neural Network adopted the gradient descent algorithm to automatically obtain the transition probability matrix, the prediction accuracy of TNNM was greatly improved. Moreover, additional antecedent memory coefficient and bias term were introduced in Transfer Neural Network, which further enhanced both the robustness and the non-linear expression ability of TNNM as well. Finally, in order to evaluate the identification performance of TNNM, intensive experiments have been executed based on two well-known public databases separately, and experimental results show that TNNM can achieve better performance than representative state-of-the-art prediction models in terms of both predictive accuracies and decline rate of accuracies. Therefore, TNNM may play an important role in key protein prediction in the future.
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Affiliation(s)
- Sisi Chen
- The First Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China
| | - Chiguo Huang
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China,*Correspondence: Chiguo Huang, ; Lei Wang, ; Shunxian Zhou,
| | - Lei Wang
- The First Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China,Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China,*Correspondence: Chiguo Huang, ; Lei Wang, ; Shunxian Zhou,
| | - Shunxian Zhou
- The First Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China,Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China,College of Information Science and Engineering, Hunan Women’s University, Changsha, Hunan, China,*Correspondence: Chiguo Huang, ; Lei Wang, ; Shunxian Zhou,
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Zhang Z, Luo Y, Jiang M, Wu D, Zhang W, Yan W, Zhao B. An efficient strategy for identifying essential proteins based on homology, subcellular location and protein-protein interaction information. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:6331-6343. [PMID: 35603404 DOI: 10.3934/mbe.2022296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
High throughput biological experiments are expensive and time consuming. For the past few years, many computational methods based on biological information have been proposed and widely used to understand the biological background. However, the processing of biological information data inevitably produces false positive and false negative data, such as the noise in the Protein-Protein Interaction (PPI) networks and the noise generated by the integration of a variety of biological information. How to solve these noise problems is the key role in essential protein predictions. An Identifying Essential Proteins model based on non-negative Matrix Symmetric tri-Factorization and multiple biological information (IEPMSF) is proposed in this paper, which utilizes only the PPI network proteins common neighbor characters to develop a weighted network, and uses the non-negative matrix symmetric tri-factorization method to find more potential interactions between proteins in the network so as to optimize the weighted network. Then, using the subcellular location and lineal homology information, the starting score of proteins is determined, and the random walk algorithm with restart mode is applied to the optimized network to mark and rank each protein. We tested the suggested forecasting model against current representative approaches using a public database. Experiment shows high efficiency of new method in essential proteins identification. The effectiveness of this method shows that it can dramatically solve the noise problems that existing in the multi-source biological information itself and cased by integrating them.
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Affiliation(s)
- Zhihong Zhang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan 410022, China
| | - Yingchun Luo
- Department of Ultrasound, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, Hunan 410008, China
| | - Meiping Jiang
- Department of Ultrasound, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, Hunan 410008, China
| | - Dongjie Wu
- Department of Banking and Finance, Monash University, Clayton, Victoria 3168, Australia
| | - Wang Zhang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou, Guangdong 510632, China
| | - Wei Yan
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan 410022, China
| | - Bihai Zhao
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan 410022, China
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Zhu X, Zhu Y, Tan Y, Chen Z, Wang L. An Iterative Method for Predicting Essential Proteins Based on Multifeature Fusion and Linear Neighborhood Similarity. Front Aging Neurosci 2022; 13:799500. [PMID: 35140599 PMCID: PMC8819145 DOI: 10.3389/fnagi.2021.799500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 12/02/2021] [Indexed: 11/13/2022] Open
Abstract
Growing evidence have demonstrated that many biological processes are inseparable from the participation of key proteins. In this paper, a novel iterative method called linear neighborhood similarity-based protein multifeatures fusion (LNSPF) is proposed to identify potential key proteins based on multifeature fusion. In LNSPF, an original protein-protein interaction (PPI) network will be constructed first based on known protein-protein interaction data downloaded from benchmark databases, based on which, topological features will be further extracted. Next, gene expression data of proteins will be adopted to transfer the original PPI network to a weighted PPI network based on the linear neighborhood similarity. After that, subcellular localization and homologous information of proteins will be integrated to extract functional features for proteins, and based on both functional and topological features obtained above. And then, an iterative method will be designed and carried out to predict potential key proteins. At last, for evaluating the predictive performance of LNSPF, extensive experiments have been done, and compare results between LNPSF and 15 state-of-the-art competitive methods have demonstrated that LNSPF can achieve satisfactory recognition accuracy, which is markedly better than that achieved by each competing method.
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Affiliation(s)
- Xianyou Zhu
- College of Computer Science and Technology, Hengyang Normal University, Hengyang, China
| | - Yaocan Zhu
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Yihong Tan
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Zhiping Chen
- College of Computer Science and Technology, Hengyang Normal University, Hengyang, China
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Lei Wang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
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Liu Y, Chen W, He Z. Essential Protein Recognition via Community Significance. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2788-2794. [PMID: 34347602 DOI: 10.1109/tcbb.2021.3102018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Essential protein plays a vital role in understanding the cellular life. With the advance in high-throughput technologies, a number of protein-protein interaction (PPI) networks have been constructed such that essential proteins can be identified from a system biology perspective. Although a series of network-based essential protein discovery methods have been proposed, these existing methods still have some drawbacks. Recently, it has been shown that the significance-based method SigEP is promising on overcoming the defects that are inherent in currently available essential protein identification methods. However, the SigEP method is developed under the unrealistic Erdös-Rényi (E-R) model and its time complexity is very high. Hence, we propose a new significance-based essential protein recognition method named EPCS in which the essential protein discovery problem is formulated as a community significance testing problem. Experimental results on four PPI networks show that EPCS performs better than nine state-of-the-art essential protein identification methods and the only significance-based essential protein identification method SigEP.
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7
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Li S, Zhang Z, Li X, Tan Y, Wang L, Chen Z. An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information. BMC Bioinformatics 2021; 22:430. [PMID: 34496745 PMCID: PMC8425031 DOI: 10.1186/s12859-021-04300-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 07/08/2021] [Indexed: 11/10/2022] Open
Abstract
Background Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively. Results In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein–protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models. Conclusions We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.
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Affiliation(s)
- Shiyuan Li
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, China.,Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, 410022, China
| | - Zhen Zhang
- College of Electronic Information and Electrical Engineering, Changsha University, Changsha, 410022, China
| | - Xueyong Li
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, China.,Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, 410022, China
| | - Yihong Tan
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, China. .,Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, 410022, China.
| | - Lei Wang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, China.,Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, 410022, China
| | - Zhiping Chen
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, China. .,Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, 410022, China.
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8
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Zhang Z, Jiang M, Wu D, Zhang W, Yan W, Qu X. A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization. Front Genet 2021; 12:709660. [PMID: 34422014 PMCID: PMC8378176 DOI: 10.3389/fgene.2021.709660] [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: 05/14/2021] [Accepted: 07/06/2021] [Indexed: 11/29/2022] Open
Abstract
Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein–protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein–protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein–protein interaction networks.
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Affiliation(s)
- Zhihong Zhang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China.,School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, China
| | - Meiping Jiang
- Department of Ultrasound, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, China
| | - Dongjie Wu
- Department of Banking and Finance, Monash University, Clayton, VIC, Australia
| | - Wang Zhang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou, China
| | - Wei Yan
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Xilong Qu
- School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, China.,Hunan Provincial Key Laboratory of Finance and Economics Big Data Science and Technology, Hunan University of Finance and Economics, Changsha, China
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9
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Zhong J, Tang C, Peng W, Xie M, Sun Y, Tang Q, Xiao Q, Yang J. A novel essential protein identification method based on PPI networks and gene expression data. BMC Bioinformatics 2021; 22:248. [PMID: 33985429 PMCID: PMC8120700 DOI: 10.1186/s12859-021-04175-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 05/06/2021] [Indexed: 02/08/2023] Open
Abstract
Background Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins. Results In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression. Conclusions We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.
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Affiliation(s)
- Jiancheng Zhong
- School of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.,Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Changsha, 410083, China
| | - Chao Tang
- School of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China
| | - Wei Peng
- College of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China
| | - Minzhu Xie
- School of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China
| | - Yusui Sun
- School of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China
| | - Qiang Tang
- College of Engineering and Design, Hunan Normal University, Changsha, 410081, China
| | - Qiu Xiao
- School of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.
| | - Jiahong Yang
- School of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.
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Dai W, Chen B, Peng W, Li X, Zhong J, Wang J. A Novel Multi-Ensemble Method for Identifying Essential Proteins. J Comput Biol 2021; 28:637-649. [PMID: 33439753 DOI: 10.1089/cmb.2020.0527] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Essential proteins possess critical functions for cell survival. Identifying essential proteins improves our understanding of how a cell works and also plays a vital role in the research fields of disease treatment and drug development. Recently, some machine-learning methods and ensemble learning methods have been proposed to identify essential proteins by introducing effective protein features. However, the ensemble learning method only used to focus on the choice of base classifiers. In this article, we propose a novel ensemble learning framework called multi-ensemble to integrate different base classifiers. The multi-ensemble method adopts the idea of multi-view learning and selects multiple base classifiers and trains those classifiers by continually adding the samples that are predicted correctly by the other base classifiers. We applied multi-ensemble to Yeast data and Escherichia coli data. The results show that our approach achieved better performance than both individual classifiers and the other ensemble learning methods.
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Affiliation(s)
- Wei Dai
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, China
| | - Bingxi Chen
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Wei Peng
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, China
| | - Xia Li
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Jiancheng Zhong
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, China
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Renau-Morata B, Carrillo L, Dominguez-Figueroa J, Vicente-Carbajosa J, Molina RV, Nebauer SG, Medina J. CDF transcription factors: plant regulators to deal with extreme environmental conditions. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:3803-3815. [PMID: 32072179 DOI: 10.1093/jxb/eraa088] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 02/03/2020] [Indexed: 05/23/2023]
Abstract
In terrestrial environments, water and nutrient availabilities and temperature conditions are highly variable, and especially in extreme environments limit survival, growth, and reproduction of plants. To sustain growth and maintain cell integrity under unfavourable environmental conditions, plants have developed a variety of biochemical and physiological mechanisms, orchestrated by a large set of stress-responsive genes and a complex network of transcription factors. Recently, cycling DOF factors (CDFs), a group of plant-specific transcription factors (TFs), were identified as components of the transcriptional regulatory networks involved in the control of abiotic stress responses. The majority of the members of this TF family are activated in response to a wide range of adverse environmental conditions in different plant species. CDFs regulate different aspects of plant growth and development such as photoperiodic flowering-time control and root and shoot growth. While most of the functional characterization of CDFs has been reported in Arabidopsis, recent data suggest that their diverse roles extend to other plant species. In this review, we integrate information related to structure and functions of CDFs in plants, with special emphasis on their role in plant responses to adverse environmental conditions.
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Affiliation(s)
- Begoña Renau-Morata
- Departamento de Producción Vegetal, Universitat Politécnica de Valencia, Camino de Vera s/n, Valencia, Spain
| | - Laura Carrillo
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo, Autopista M40 (km 38), Madrid, Spain
| | - Jose Dominguez-Figueroa
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo, Autopista M40 (km 38), Madrid, Spain
| | - Jesús Vicente-Carbajosa
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo, Autopista M40 (km 38), Madrid, Spain
| | - Rosa V Molina
- Departamento de Producción Vegetal, Universitat Politécnica de Valencia, Camino de Vera s/n, Valencia, Spain
| | - Sergio G Nebauer
- Departamento de Producción Vegetal, Universitat Politécnica de Valencia, Camino de Vera s/n, Valencia, Spain
| | - Joaquín Medina
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo, Autopista M40 (km 38), Madrid, Spain
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12
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Zhao B, Hu S, Liu X, Xiong H, Han X, Zhang Z, Li X, Wang L. A Novel Computational Approach for Identifying Essential Proteins From Multiplex Biological Networks. Front Genet 2020; 11:343. [PMID: 32373163 PMCID: PMC7186452 DOI: 10.3389/fgene.2020.00343] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 03/23/2020] [Indexed: 11/13/2022] Open
Abstract
The identification of essential proteins can help in understanding the minimum requirements for cell survival and development. Ever-increasing amounts of high-throughput data provide us with opportunities to detect essential proteins from protein interaction networks (PINs). Existing network-based approaches are limited by the poor quality of the underlying PIN data, which exhibits high rates of false positive and false negative results. To overcome this problem, researchers have focused on the prediction of essential proteins by combining PINs with other biological data, which has led to the emergence of various interactions between proteins. It remains challenging, however, to use aggregated multiplex interactions within a single analysis framework to identify essential proteins. In this study, we created a multiplex biological network (MON) by initially integrating PINs, protein domains, and gene expression profiles. Next, we proposed a new approach to discover essential proteins by extending the random walk with restart algorithm to the tensor, which provides a data model representation of the MON. In contrast to existing approaches, the proposed MON approach considers for the importance of nodes and the different types of interactions between proteins during the iteration. MON was implemented to identify essential proteins within two yeast PINs. Our comprehensive experimental results demonstrated that MON outperformed 11 other state-of-the-art approaches in terms of precision-recall curve, jackknife curve, and other criteria.
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Affiliation(s)
- Bihai Zhao
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China.,Hunan Provincial Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, China.,Hunan Provincial Key Laboratory of Nutrition and Quality Control of Aquatic Animals, Changsha University, Changsha, China
| | - Sai Hu
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Xiner Liu
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Huijun Xiong
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Xiao Han
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Zhihong Zhang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China.,Hunan Provincial Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, China
| | - Xueyong Li
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Lei Wang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China.,Hunan Provincial Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, China
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Network Embedding the Protein-Protein Interaction Network for Human Essential Genes Identification. Genes (Basel) 2020; 11:genes11020153. [PMID: 32023848 PMCID: PMC7074227 DOI: 10.3390/genes11020153] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/27/2020] [Accepted: 01/29/2020] [Indexed: 11/18/2022] Open
Abstract
Essential genes are a group of genes that are indispensable for cell survival and cell fertility. Studying human essential genes helps scientists reveal the underlying biological mechanisms of a human cell but also guides disease treatment. Recently, the publication of human essential gene data makes it possible for researchers to train a machine-learning classifier by using some features of the known human essential genes and to use the classifier to predict new human essential genes. Previous studies have found that the essentiality of genes closely relates to their properties in the protein–protein interaction (PPI) network. In this work, we propose a novel supervised method to predict human essential genes by network embedding the PPI network. Our approach implements a bias random walk on the network to get the node network context. Then, the node pairs are input into an artificial neural network to learn their representation vectors that maximally preserves network structure and the properties of the nodes in the network. Finally, the features are put into an SVM classifier to predict human essential genes. The prediction results on two human PPI networks show that our method achieves better performance than those that refer to either genes’ sequence information or genes’ centrality properties in the network as input features. Moreover, it also outperforms the methods that represent the PPI network by other previous approaches.
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Wen QF, Liu S, Dong C, Guo HX, Gao YZ, Guo FB. Geptop 2.0: An Updated, More Precise, and Faster Geptop Server for Identification of Prokaryotic Essential Genes. Front Microbiol 2019; 10:1236. [PMID: 31214154 PMCID: PMC6558110 DOI: 10.3389/fmicb.2019.01236] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Accepted: 05/17/2019] [Indexed: 12/16/2022] Open
Abstract
Geptop has performed effectively in the identification of prokaryotic essential genes since its first release in 2013. It estimates gene essentiality for prokaryotes based on orthology and phylogeny. Genome-scale essentiality data of more prokaryotic species are available, and the information has been collected into public essential gene repositories such as DEG and OGEE. A faster and more accurate toolkit is needed to meet the increasing prokaryotic genome data. We updated Geptop by supplementing more validated essentiality data into reference set (from 19 to 37 species), and introducing multi-process technology to accelerate the computing speed. Compared with Geptop 1.0 and other gene essentiality prediction models, Geptop 2.0 can generate more stable predictions and finish the computation in a shorter time. The software is available both as an online server and a downloadable standalone application. We hope that the improved Geptop 2.0 will facilitate researches in gene essentiality and the development of novel antibacterial drugs. The gene essentiality prediction tool is available at http://cefg.uestc.cn/geptop.
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Affiliation(s)
- Qing-Feng Wen
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuo Liu
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chuan Dong
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hai-Xia Guo
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yi-Zhou Gao
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng-Biao Guo
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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