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Zafar A, Wajid B, Shabbir A, Gohar Awan F, Ahsan M, Ahmad S, Wajid I, Anwar F, Mazhar F. Unearthing Insights into Metabolic Syndrome by Linking Drugs, Targets, and Gene Expressions Using Similarity Measures and Graph Theory. Curr Comput Aided Drug Des 2024; 20:773-783. [PMID: 37592790 DOI: 10.2174/1573409920666230817101913] [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: 01/05/2023] [Revised: 06/13/2023] [Accepted: 07/05/2023] [Indexed: 08/19/2023]
Abstract
AIMS AND OBJECTIVES Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive in silico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS. METHODS For this purpose, we (i) investigated associated drugs and pathways for MetS, (ii) employed eight different similarity measures to construct eight gene regulatory networks, (iii) chose an optimal network, where a maximum number of drug targets were central, (iv) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (v) employed these candidate genes to propose suitable drugs. RESULTS Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS. CONCLUSION Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.
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Affiliation(s)
- Alwaz Zafar
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
| | - Bilal Wajid
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
- Department of Electrical Engineering, University of Engineering and Technology, Lahore, 54000, Pakistan
| | - Ans Shabbir
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
| | - Fahim Gohar Awan
- Department of Electrical Engineering, University of Engineering and Technology, Lahore, 54000, Pakistan
| | - Momina Ahsan
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
| | - Sarfraz Ahmad
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
| | - Imran Wajid
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
- Department of Social Sciences, Istanbul Commerce University, Istanbul, Turkey
| | - Faria Anwar
- Outpatient Department, Mayo Hospital, Lahore, 54000, Pakistan
| | - Fazeelat Mazhar
- Department of Biomedical, Electrical and System Engineering, University of Bologna, Cesena Campus, Bologna, Italy
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Li G, Luo X, Hu Z, Wu J, Peng W, Liu J, Zhu X. Essential proteins discovery based on dominance relationship and neighborhood similarity centrality. Health Inf Sci Syst 2023; 11:55. [PMID: 37981988 PMCID: PMC10654316 DOI: 10.1007/s13755-023-00252-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/13/2023] [Indexed: 11/21/2023] Open
Abstract
Essential proteins play a vital role in development and reproduction of cells. The identification of essential proteins helps to understand the basic survival of cells. Due to time-consuming, costly and inefficient with biological experimental methods for discovering essential proteins, computational methods have gained increasing attention. In the initial stage, essential proteins are mainly identified by the centralities based on protein-protein interaction (PPI) networks, which limit their identification rate due to many false positives in PPI networks. In this study, a purified PPI network is firstly introduced to reduce the impact of false positives in the PPI network. Secondly, by analyzing the similarity relationship between a protein and its neighbors in the PPI network, a new centrality called neighborhood similarity centrality (NSC) is proposed. Thirdly, based on the subcellular localization and orthologous data, the protein subcellular localization score and ortholog score are calculated, respectively. Fourthly, by analyzing a large number of methods based on multi-feature fusion, it is found that there is a special relationship among features, which is called dominance relationship, then, a novel model based on dominance relationship is proposed. Finally, NSC, subcellular localization score, and ortholog score are fused by the dominance relationship model, and a new method called NSO is proposed. In order to verify the performance of NSO, the seven representative methods (ION, NCCO, E_POC, SON, JDC, PeC, WDC) are compared on yeast datasets. The experimental results show that the NSO method has higher identification rate than other methods.
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Affiliation(s)
- Gaoshi Li
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
| | - Xinlong Luo
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
| | - Zhipeng Hu
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
| | - Jingli Wu
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
| | - Wei Peng
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500 Yunnan China
| | - Jiafei Liu
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
| | - Xiaoshu Zhu
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China
- School of Computer and Information Security & School of Software Engineering, Guilin University of Electronic Science and Technology, Guilin, China
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Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN. Cells 2022; 11:cells11172648. [PMID: 36078056 PMCID: PMC9454873 DOI: 10.3390/cells11172648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/18/2022] [Accepted: 08/22/2022] [Indexed: 11/25/2022] Open
Abstract
Proteins are vital for the significant cellular activities of living organisms. However, not all of them are essential. Identifying essential proteins through different biological experiments is relatively more laborious and time-consuming than the computational approaches used in recent times. However, practical implementation of conventional scientific methods sometimes becomes challenging due to poor performance impact in specific scenarios. Thus, more developed and efficient computational prediction models are required for essential protein identification. An effective methodology is proposed in this research, capable of predicting essential proteins in a refined yeast protein–protein interaction network (PPIN). The rule-based refinement is done using protein complex and local interaction density information derived from the neighborhood properties of proteins in the network. Identification and pruning of non-essential proteins are equally crucial here. In the initial phase, careful assessment is performed by applying node and edge weights to identify and discard the non-essential proteins from the interaction network. Three cut-off levels are considered for each node and edge weight for pruning the non-essential proteins. Once the PPIN has been filtered out, the second phase starts with two centralities-based approaches: (1) local interaction density (LID) and (2) local interaction density with protein complex (LIDC), which are successively implemented to identify the essential proteins in the yeast PPIN. Our proposed methodology achieves better performance in comparison to the existing state-of-the-art techniques.
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Schapke J, Tavares A, Recamonde-Mendoza M. EPGAT: Gene Essentiality Prediction With Graph Attention Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1615-1626. [PMID: 33497339 DOI: 10.1109/tcbb.2021.3054738] [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
Identifying essential genes and proteins is a critical step towards a better understanding of human biology and pathology. Computational approaches helped to mitigate experimental constraints by exploring machine learning (ML) methods and the correlation of essentiality with biological information, especially protein-protein interaction (PPI) networks, to predict essential genes. Nonetheless, their performance is still limited, as network-based centralities are not exclusive proxies of essentiality, and traditional ML methods are unable to learn from non-euclidean domains such as graphs. Given these limitations, we proposed EPGAT, an approach for Essentiality Prediction based on Graph Attention Networks (GATs), which are attention-based Graph Neural Networks (GNNs), operating on graph-structured data. Our model directly learns gene essentiality patterns from PPI networks, integrating additional evidence from multiomics data encoded as node attributes. We benchmarked EPGAT for four organisms, including humans, accurately predicting gene essentiality with ROC AUC score ranging from 0.78 to 0.97. Our model significantly outperformed network-based and shallow ML-based methods and achieved a very competitive performance against the state-of-the-art node2vec embedding method. Notably, EPGAT was the most robust approach in scenarios with limited and imbalanced training data. Thus, the proposed approach offers a powerful and effective way to identify essential genes and proteins.
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Semi-Local Integration Measure of Node Importance. MATHEMATICS 2022. [DOI: 10.3390/math10030405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Numerous centrality measures have been introduced as tools to determine the importance of nodes in complex networks, reflecting various network properties, including connectivity, survivability, and robustness. In this paper, we introduce Semi-Local Integration (SLI), a node centrality measure for undirected and weighted graphs that takes into account the coherence of the locally connected subnetwork and evaluates the integration of nodes within their neighbourhood. We illustrate SLI node importance differentiation among nodes in lexical networks and demonstrate its potential in natural language processing (NLP). In the NLP task of sense identification and sense structure analysis, the SLI centrality measure evaluates node integration and provides the necessary local resolution by differentiating the importance of nodes to a greater extent than standard centrality measures. This provides the relevant topological information about different subnetworks based on relatively local information, revealing the more complex sense structure. In addition, we show how the SLI measure can improve the results of sentiment analysis. The SLI measure has the potential to be used in various types of complex networks in different research areas.
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Sonsungsan P, Chantanakool P, Suratanee A, Buaboocha T, Comai L, Chadchawan S, Plaimas K. Identification of Key Genes in 'Luang Pratahn', Thai Salt-Tolerant Rice, Based on Time-Course Data and Weighted Co-expression Networks. FRONTIERS IN PLANT SCIENCE 2021; 12:744654. [PMID: 34925399 PMCID: PMC8675607 DOI: 10.3389/fpls.2021.744654] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/01/2021] [Indexed: 05/13/2023]
Abstract
Salinity is an important environmental factor causing a negative effect on rice production. To prevent salinity effects on rice yields, genetic diversity concerning salt tolerance must be evaluated. In this study, we investigated the salinity responses of rice (Oryza sativa) to determine the critical genes. The transcriptomes of 'Luang Pratahn' rice, a local Thai rice variety with high salt tolerance, were used as a model for analyzing and identifying the key genes responsible for salt-stress tolerance. Based on 3' Tag-Seq data from the time course of salt-stress treatment, weighted gene co-expression network analysis was used to identify key genes in gene modules. We obtained 1,386 significantly differentially expressed genes in eight modules. Among them, six modules indicated a significant correlation within 6, 12, or 48h after salt stress. Functional and pathway enrichment analysis was performed on the co-expressed genes of interesting modules to reveal which genes were mainly enriched within important functions for salt-stress responses. To identify the key genes in salt-stress responses, we considered the two-state co-expression networks, normal growth conditions, and salt stress to investigate which genes were less important in a normal situation but gained more impact under stress. We identified key genes for the response to biotic and abiotic stimuli and tolerance to salt stress. Thus, these novel genes may play important roles in salinity tolerance and serve as potential biomarkers to improve salt tolerance cultivars.
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Affiliation(s)
- Pajaree Sonsungsan
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
| | - Pheerawat Chantanakool
- Center of Excellence in Environment and Plant Physiology, Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Apichat Suratanee
- Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
| | - Teerapong Buaboocha
- Molecular Crop Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Luca Comai
- Department of Plant Biology, College of Biological Sciences, College of Biological Sciences, University of California, Davis, Davis, CA, United States
| | - Supachitra Chadchawan
- Center of Excellence in Environment and Plant Physiology, Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Kitiporn Plaimas
- Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
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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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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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Liu D, Huang K, Wu D, Zhang S. A New Method of Identifying Core Designers and Teams Based on the Importance and Similarity of Networks. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:3717733. [PMID: 34335714 PMCID: PMC8318751 DOI: 10.1155/2021/3717733] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/04/2021] [Accepted: 07/13/2021] [Indexed: 11/18/2022]
Abstract
In the process of product collaborative design, the association between designers can be described by a complex network. Exploring the importance of the nodes and the rules of information dissemination in such networks is of great significance for distinguishing its core designers and potential designer teams, as well as for accurate recommendations of collaborative design tasks. Based on the neighborhood similarity model, combined with the idea of network information propagation, and with the help of the ReLU function, this paper proposes a new method for judging the importance of nodes-LLSR. This method not only reflects the local connection characteristics of nodes but also considers the trust degree of network propagation, and the neighbor nodes' information is used to modify the node value. Next, in order to explore potential teams, an LA-LPA algorithm based on node importance and node similarity was proposed. Before the iterative update, all nodes were randomly sorted to get an update sequence which was replaced by the node importance sequence. When there are multiple largest neighbor labels in the propagation process, the label with the highest similarity is selected for update. The experimental results in the related networks show that the LLSR algorithm can better identify the core nodes in the network, and the LA-LPA algorithm has greatly improved the stability of the original LPA algorithm and has stably mined potential teams in the network.
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Affiliation(s)
- Dianting Liu
- College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541004, China
- College of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
| | - Kangzheng Huang
- College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541004, China
| | - Danling Wu
- College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541004, China
| | - Shenglan Zhang
- College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541004, China
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Transcriptomic Analysis Exploring the Molecular Mechanisms of Hanchuan Zupa Granules in Alleviating Asthma in Rat. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2021; 2021:5584099. [PMID: 34285702 PMCID: PMC8275397 DOI: 10.1155/2021/5584099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/22/2021] [Indexed: 12/15/2022]
Abstract
Objective To investigate the molecular mechanisms of HCZP treatment of asthma. Materials and Methods Thirty Sprague Dawley (SD) rats were divided into normal, asthma, and HCZP groups (n = 10). The asthma model was sensitized by 1 mg ovalbumin (OVA)/aluminum hydroxide Al(OH)3mixture and then challenged with 1% aerosolized OVA for four weeks. Rats in the HCZP group received 10.08 g/kg/d HCZP for four weeks during OVA challenge. Then, lung tissues of rats in each group were collected for RNA sequencing. Moreover, the expression level of some core genes was detected by using western blotting and immunohistochemistry. Results Inflammatory cell infiltration and pathological damage of the lungs improved in the HCZP group. Compared with the asthma group (0.049 ± 0.002 mm2/mm; 0.036 ± 0.006 mm2/mm; and 0.014 ± 0.001 mm2/mm), total wall thickness (0.042 ± 0.001 mm2/mm), inner wall thickness (0.013 ± 0.001 mm2/mm), and smooth muscle layer thickness (0.012 ± 0.001 mm2/mm) significantly decreased in the HCZP group. Bioinformatics analysis showed that hub genes such as bradykinin receptor B2 (Bdkrb2) and CD4 molecule (Cd4) had different expression patterns between model and HCZP groups. Two transcription factors, forkhead box Q1 (Foxq1) and nuclear factor of activated T cells 2 (Nfatc2), served important regulatory roles in asthma. Compared with the model group, Bdkrb2 protein expression increased and Nfatc2 protein expression decreased in the HCZP group. Discussion and Conclusion. HCZP could alleviate asthma via regulating the expression of several hub genes, which might serve as therapeutic targets for asthma. However, the mechanism of these genes will be studied in the future.
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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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Decano JL, Singh SA, Gasparotto Bueno C, Ho Lee L, Halu A, Chelvanambi S, Matamalas JT, Zhang H, Mlynarchik AK, Qiao J, Sharma A, Mukai S, Wang J, Anderson DG, Ozaki CK, Libby P, Aikawa E, Aikawa M. Systems Approach to Discovery of Therapeutic Targets for Vein Graft Disease: PPARα Pivotally Regulates Metabolism, Activation, and Heterogeneity of Macrophages and Lesion Development. Circulation 2021; 143:2454-2470. [PMID: 33821665 PMCID: PMC8212880 DOI: 10.1161/circulationaha.119.043724] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Supplemental Digital Content is available in the text. Vein graft failure remains a common clinical challenge. We applied a systems approach in mouse experiments to discover therapeutic targets for vein graft failure.
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Affiliation(s)
- Julius L Decano
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Sasha A Singh
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Cauê Gasparotto Bueno
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Lang Ho Lee
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Arda Halu
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Channing Division of Network Medicine (A.H., A.S., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Sarvesh Chelvanambi
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Joan T Matamalas
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Hengmin Zhang
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Andrew K Mlynarchik
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Jiao Qiao
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Amitabh Sharma
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Channing Division of Network Medicine (A.H., A.S., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Shin Mukai
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Jianguo Wang
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Daniel G Anderson
- Institutes for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge (D.G.A.)
| | - C Keith Ozaki
- Department of Medicine, Division of Vascular and Endovascular Surgery, Department of Surgery (C.K.O.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Peter Libby
- Center for Excellence in Vascular Biology (P.L., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Center for Excellence in Vascular Biology (P.L., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Department of Human Pathology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health, Russia (E.A., M.A.)
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division (J.L.D., S.A.S., C.G.B., L.H.L., A.H., S.C., J.T.M., H.Z., A.K.M., J.Q., A.S., S.M., J.W., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Channing Division of Network Medicine (A.H., A.S., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Center for Excellence in Vascular Biology (P.L., E.A., M.A.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Department of Human Pathology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health, Russia (E.A., M.A.)
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Wang LL, Liao C, Li XQ, Dai R, Ren QW, Shi HL, Wang XP, Feng XS, Chao X. Systems Pharmacology-Based Identification of Mechanisms of Action of Bolbostemma paniculatum for the Treatment of Hepatocellular Carcinoma. Med Sci Monit 2021; 27:e927624. [PMID: 33436534 PMCID: PMC7812697 DOI: 10.12659/msm.927624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background Traditional Chinese medicine has widely used Bolbostemma paniculatum to treat diseases, including cancer, but its underlying mechanisms remain unclear. The present study aimed to elucidate the potential pharmacological mechanisms of “Tu Bei Mu” (TBM), the Chinese name for Bolbostemmatis Rhizoma, the dry tuber of B. paniculatum, for the treatment of hepatocellular carcinoma (HCC). Material/Methods The active components and putative therapeutic targets of TBM were explored using SwissTargetPrediction, Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and Search Tool for Interactions of Chemicals (STITCH). The HCC-related target database was built using DrugBank, DisGeNet, Online Mendelian Inheritance in Man (OMIM), and Therapeutic Target Database (TTD). A protein–protein interaction network of the common targets was constructed, based on the matches between TBM potential targets and HCC-related targets, using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the cluster networks were used to elucidate the biological functions of TBM. Results Pharmacological network diagrams of the TBM compound-target network and HCC-related target network were successfully constructed. A total of 22 active components, 191 predicted biological targets of TBM, and 3775 HCC-related targets were identified. Through construction of an HCC-related target database and a protein–protein interaction network of the common targets, TBM was predicted to be effective in treating HCC mainly through the PI3K-Akt, HIF-1, p53, and PPAR signaling pathways. Conclusions The PI3K/Akt, HIF1, p53, and PPAR pathways may play vital roles in TBM treatment of HCC. Also, the potential anti-cancer effect of TBM on HCC appears to stem from the synergetic effect of multiple targets and mechanisms.
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Affiliation(s)
- Lan-Lan Wang
- College of Basic Medicine, Shaanxi University of Chinese Medicine, Xi'an, Shaanxi, China (mainland)
| | - Chen Liao
- Department of Pharmacology, Yunnan University of Chinese Medicine, Kunming, Yunnan, China (mainland)
| | - Xiao-Qiang Li
- Key Laboratory of Gastrointestinal Pharmacology of Chinese Materia Medica of the State Administration of Traditional Chinese Medicine, Department of Pharmacology, School of Pharmacy, Fourth Military Medical University, Xi'an, Shaanxi, China (mainland)
| | - Rong Dai
- Department of Pharmacology, Yunnan University of Chinese Medicine, Kunming, Yunnan, China (mainland)
| | - Qing-Wei Ren
- The Second Affiliated Hospital of Shaanxi University of Chinese Medicine, Xi'an, Shaanxi, China (mainland)
| | - Hai-Long Shi
- College of Basic Medicine, Shaanxi University of Chinese Medicine, Xi'an, Shaanxi, China (mainland)
| | - Xiao-Ping Wang
- College of Basic Medicine, Shaanxi University of Chinese Medicine, Xi'an, Shaanxi, China (mainland)
| | - Xue-Song Feng
- College of Basic Medicine, Shaanxi University of Chinese Medicine, Xi'an, Shaanxi, China (mainland)
| | - Xu Chao
- College of Basic Medicine, Shaanxi University of Chinese Medicine, Xi'an, Shaanxi, China (mainland).,The Second Affiliated Hospital of Shaanxi University of Chinese Medicine, Xi'an, Shaanxi, China (mainland)
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Zeng M, Li M, Fei Z, Wu FX, Li Y, Pan Y, Wang J. A Deep Learning Framework for Identifying Essential Proteins by Integrating Multiple Types of Biological Information. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:296-305. [PMID: 30736002 DOI: 10.1109/tcbb.2019.2897679] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Computational methods including centrality and machine learning-based methods have been proposed to identify essential proteins for understanding the minimum requirements of the survival and evolution of a cell. In centrality methods, researchers are required to design a score function which is based on prior knowledge, yet is usually not sufficient to capture the complexity of biological information. In machine learning-based methods, some selected biological features cannot represent the complete properties of biological information as they lack a computational framework to automatically select features. To tackle these problems, we propose a deep learning framework to automatically learn biological features without prior knowledge. We use node2vec technique to automatically learn a richer representation of protein-protein interaction (PPI) network topologies than a score function. Bidirectional long short term memory cells are applied to capture non-local relationships in gene expression data. For subcellular localization information, we exploit a high dimensional indicator vector to characterize their feature. To evaluate the performance of our method, we tested it on PPI network of S. cerevisiae. Our experimental results demonstrate that the performance of our method is better than traditional centrality methods and is superior to existing machine learning-based methods. To explore which of the three types of biological information is the most vital element, we conduct an ablation study by removing each component in turn. Our results show that the PPI network embedding contributes most to the improvement. In addition, gene expression profiles and subcellular localization information are also helpful to improve the performance in identification of essential proteins.
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15
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Screening and identification of potential prognostic biomarkers in bladder urothelial carcinoma: Evidence from bioinformatics analysis. GENE REPORTS 2020. [DOI: 10.1016/j.genrep.2020.100658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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16
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Bashiri H, Rahmani H, Bashiri V, Módos D, Bender A. EMDIP: An Entropy Measure to Discover Important Proteins in PPI networks. Comput Biol Med 2020; 120:103740. [PMID: 32421645 DOI: 10.1016/j.compbiomed.2020.103740] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 03/30/2020] [Accepted: 03/30/2020] [Indexed: 12/24/2022]
Abstract
Discovering important proteins in Protein-Protein Interaction (PPI) networks has attracted a lot of attention in recent years. Most of the previous work applies different network centrality measures such as Closeness, Betweenness, PageRank and many others to discover the most influential proteins in PPI networks. Although entropy is a well-known graph-based method in computer science, according to our knowledge, it is not used in the biology domain for this purpose. In this paper, first, we annotate the human PPI network with available annotation data. Second, we introduce a new concept called annotation-context that describes each protein according to annotation data of its neighbors. Third, we apply an entropy measure to discover proteins with varied annotation-context. Empirical results indicate that our proposed method succeeded in (1) differentiating essential and non-essential proteins in PPI networks with annotation data; (2) outperforming centrality measures in the task of discovering essential nodes; (3) predicting new annotated proteins based on existing annotation data.
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Affiliation(s)
- Hamid Bashiri
- School of Computer engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran
| | - Hossein Rahmani
- School of Computer engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran.
| | - Vahid Bashiri
- School of Computer engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran
| | - Dezső Módos
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
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Zeng M, Li M, Wu FX, Li Y, Pan Y. DeepEP: a deep learning framework for identifying essential proteins. BMC Bioinformatics 2019; 20:506. [PMID: 31787076 PMCID: PMC6886168 DOI: 10.1186/s12859-019-3076-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Results We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins. Conclusion We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.
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Affiliation(s)
- Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, 410083, People's Republic of China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, 410083, People's Republic of China.
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9, Canada
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA23529, USA
| | - Yi Pan
- Department of Computer Science, Georgia State University, Atlanta, GA30302, USA
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Zhang Z, Ruan J, Gao J, Wu FX. Predicting essential proteins from protein-protein interactions using order statistics. J Theor Biol 2019; 480:274-283. [PMID: 31251944 DOI: 10.1016/j.jtbi.2019.06.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 03/24/2019] [Accepted: 06/24/2019] [Indexed: 12/11/2022]
Abstract
Many computational methods have been proposed to predict essential proteins from protein-protein interaction (PPI) networks. However, it is still challenging to improve the prediction accuracy. In this study, we propose a new method, esPOS (essential proteins Predictor using Order Statistics) to predict essential proteins from PPI networks. Firstly, we refine the networks by using gene expression information and subcellular localization information. Secondly, we design some new features, which combine the protein predicted secondary structure with PPI network. We show that these new features are useful to predict essential proteins. Thirdly, we optimize these features by using a greedy method, and combine the optimized features by order statistic method. Our method achieves the prediction accuracy of 0.76-0.79 on two network datasets. The proposed method is available at https://sourceforge.net/projects/espos/.
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Affiliation(s)
- Zhaopeng Zhang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China.
| | - Jishou Ruan
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China.
| | - Jianzhao Gao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, 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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