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Liang Y, Yang S, Zheng L, Wang H, Zhou J, Huang S, Yang L, Zuo Y. Research progress of reduced amino acid alphabets in protein analysis and prediction. Comput Struct Biotechnol J 2022; 20:3503-3510. [PMID: 35860409 PMCID: PMC9284397 DOI: 10.1016/j.csbj.2022.07.001] [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: 03/14/2022] [Revised: 06/30/2022] [Accepted: 07/01/2022] [Indexed: 11/29/2022] Open
Abstract
A comprehensive summary of the literature on the reduced amino acid alphabets. A systematic review of the development history of reduced amino acid alphabets. Rich application cases of amino acid reduction alphabets are described in the article. A detailed analysis of the properties and uses of the reduced amino acid alphabets.
Proteins are the executors of cellular physiological activities, and accurate structural and function elucidation are crucial for the refined mapping of proteins. As a feature engineering method, the reduction of amino acid composition is not only an important method for protein structure and function analysis, but also opens a broad horizon for the complex field of machine learning. Representing sequences with fewer amino acid types greatly reduces the complexity and noise of traditional feature engineering in dimension, and provides more interpretable predictive models for machine learning to capture key features. In this paper, we systematically reviewed the strategy and method studies of the reduced amino acid (RAA) alphabets, and summarized its main research in protein sequence alignment, functional classification, and prediction of structural properties, respectively. In the end, we gave a comprehensive analysis of 672 RAA alphabets from 74 reduction methods.
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Affiliation(s)
- Yuchao Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Siqi Yang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Hao Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Jian Zhou
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Shenghui Huang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
- Corresponding authors.
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
- Corresponding authors.
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2
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Tamadaddi C, Verma AK, Zambare V, Vairagkar A, Diwan D, Sahi C. J-like protein family of Arabidopsis thaliana: the enigmatic cousins of J-domain proteins. PLANT CELL REPORTS 2022; 41:1343-1355. [PMID: 35290497 DOI: 10.1007/s00299-022-02857-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 02/26/2022] [Indexed: 06/14/2023]
Abstract
J-like proteins (JLPs) are emerging as ancillaries to the cellular chaperone network. They modulate functions of Hsp70:J-domain protein (JDP) systems in novel ways thereby having key roles in diverse plant processes. J-domain proteins (JDPs) form an obligate co-chaperone partnership with Hsp70s with their highly conserved J-domain to steer protein quality control processes in the cell. The HPD motif between helix II and helix III of the J-domain is crucial for JDP's interaction with Hsp70s. According to the most recent classification, J-like proteins (JLPs) form an extended class of the JDP family possessing a degenerate J-domain with the HPD motif non-conservatively replaced by other amino acid residues and hence are not able to interact with Hsp70s. Considering this most updated and acceptable JLP classification, we identified 21 JLPs in Arabidopsis thaliana that share a structurally conserved J-like domain (JLD), but lack the HPD motif. Analysis of publicly available gene expression data as well as real-time quantitative PCR performed for a few selected JLPs implicated some of these proteins in growth, development and stress response. Here, we summarize the current state of knowledge on plant JLPs and their involvement in vital plant cellular/metabolic processes, including chloroplast division, mitochondrial protein import and flowering. Finally, we propose possible modes of action for these highly elusive proteins and other DnaJ-related proteins (DNAJRs) in regulating the Hsp70 chaperone network.
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Affiliation(s)
- Chetana Tamadaddi
- Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, India
- Department of Biology, Eberly College of Science, The Pennsylvania State University, University Park, PA, USA
| | - Amit K Verma
- Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, India
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Vyankatesh Zambare
- School of Biotechnology and Bioinformatics, D Y Patil Deemed to be University, Navi Mumbai, India
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Avanti Vairagkar
- Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, India
| | - Danish Diwan
- Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, India
- Department of Biology, University of Alabama, Birmingham, AL, USA
| | - Chandan Sahi
- Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, India.
- IISER Bhopal, Room Number 117, AB3, Bhopal Bypass Road, Bhopal, 462066, MP, India.
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3
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Zhang Z, Wang J, Liu J. DeepRTCP: Predicting ATP-Binding Cassette Transporters Based on 1-Dimensional Convolutional Network. Front Cell Dev Biol 2021; 8:614080. [PMID: 33598454 PMCID: PMC7882686 DOI: 10.3389/fcell.2020.614080] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 12/24/2020] [Indexed: 11/13/2022] Open
Abstract
ATP-binding cassette (ABC) transporters can promote cells to absorb nutrients and excrete harmful substances. It plays a vital role in the transmembrane transport of macromolecules. Therefore, the identification of ABC transporters is of great significance for the biological research. This paper will introduce a novel method called DeepRTCP. DeepRTCP uses the deep convolutional neural network and a feature combined of reduced amino acid alphabet based tripeptide composition and PSSM to recognize ABC transporters. We constructed a dataset named ABC_2020. It contains the latest ABC transporters downloaded from Uniprot. We performed 10-fold cross-validation on DeepRTCP, and the average accuracy of DeepRTCP was 95.96%. Compared with the start-of-the-art method for predicting ABC transporters, DeepRTCP improved the accuracy by 9.29%. It is anticipated that DeepRTCP can be used as an effective ABC transporter classifier which provides a reliable guidance for the research of ABC transporters.
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Affiliation(s)
- Zhaoxi Zhang
- School of Computer Science, Inner Mongolia University, Hohhot, China
| | - Juan Wang
- School of Computer Science, Inner Mongolia University, Hohhot, China.,Stage Key Laboratories of Reproductive Regulation & Breeding of Grassland Livestock, Hohhot, China
| | - Jiameng Liu
- School of Computer Science, Inner Mongolia University, Hohhot, China
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Li H, Du H, Wang X, Gao P, Liu Y, Lin W. Remarks on Computational Method for Identifying Acid and Alkaline Enzymes. Curr Pharm Des 2020; 26:3105-3114. [PMID: 32552636 DOI: 10.2174/1381612826666200617170826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Accepted: 05/07/2020] [Indexed: 11/22/2022]
Abstract
The catalytic efficiency of the enzyme is thousands of times higher than that of ordinary catalysts. Thus, they are widely used in industrial and medical fields. However, enzymes with protein structure can be destroyed and inactivated in high temperature, over acid or over alkali environment. It is well known that most of enzymes work well in an environment with pH of 6-8, while some special enzymes remain active only in an alkaline environment with pH > 8 or an acidic environment with pH < 6. Therefore, the identification of acidic and alkaline enzymes has become a key task for industrial production. Because of the wide varieties of enzymes, it is hard work to determine the acidity and alkalinity of the enzyme by experimental methods, and even this task cannot be achieved. Converting protein sequences into digital features and building computational models can efficiently and accurately identify the acidity and alkalinity of enzymes. This review summarized the progress of the digital features to express proteins and computational methods to identify acidic and alkaline enzymes. We hope that this paper will provide more convenience, ideas, and guides for computationally classifying acid and alkaline enzymes.
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Affiliation(s)
- Hongfei Li
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Haoze Du
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, 27109, United States
| | - Xianfang Wang
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Peng Gao
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Yifeng Liu
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Weizhong Lin
- Department of Computer Science, University of Missouri, Columbia, MO, 65211, United States
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Liu T, Tang H. A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite. Curr Pharm Des 2020; 26:3049-3058. [PMID: 32156226 DOI: 10.2174/1381612826666200310122324] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Accepted: 02/10/2020] [Indexed: 11/22/2022]
Abstract
The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.
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Affiliation(s)
- Ting Liu
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou 646000, China
| | - Hua Tang
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou 646000, China
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6
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pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset. Genomics 2019; 111:1274-1282. [DOI: 10.1016/j.ygeno.2018.08.007] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 08/14/2018] [Accepted: 08/16/2018] [Indexed: 12/17/2022]
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7
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Li SH, Guan ZX, Zhang D, Zhang ZM, Huang J, Yang W, Lin H. Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods. Med Chem 2019; 16:605-619. [PMID: 31584379 DOI: 10.2174/1573406415666191004101913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 06/25/2019] [Accepted: 08/23/2019] [Indexed: 01/28/2023]
Abstract
Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance-especially the multidrug-resistant (MDR) and extensively drug-resistance (XDR)-poses a huge threat to the treatment. However, traditional biochemical experimental method to tackle TB is time-consuming and costly. Benefited by the appearance of the enormous genomic and proteomic sequence data, TB can be treated via sequence-based biological computational approach-bioinformatics. Studies on predicting subcellular localization of mycobacterial protein (MBP) with high precision and efficiency may help figure out the biological function of these proteins and then provide useful insights for protein function annotation as well as drug design. In this review, we reported the progress that has been made in computational prediction of subcellular localization of MBP including the following aspects: 1) Construction of benchmark datasets. 2) Methods of feature extraction. 3) Techniques of feature selection. 4) Application of several published prediction algorithms. 5) The published results. 6) The further study on prediction of subcellular localization of MBP.
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Affiliation(s)
- Shi-Hao Li
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Huang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wuritu Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Development and Planning Department, Inner Mongolia University, Hohhot, P.R. China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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8
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Verma AK, Tamadaddi C, Tak Y, Lal SS, Cole SJ, Hines JK, Sahi C. The expanding world of plant J-domain proteins. CRITICAL REVIEWS IN PLANT SCIENCES 2019; 38:382-400. [PMID: 33223602 PMCID: PMC7678915 DOI: 10.1080/07352689.2019.1693716] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Plants maintain cellular proteostasis during different phases of growth and development despite a barrage of biotic and abiotic stressors in an ever-changing environment. This requires a collaborative effort of a cadre of molecular chaperones. Hsp70s and their obligate co-chaperones, J-domain proteins (JDPs), are arguably the most ubiquitous and formidable components of the cellular chaperone network, facilitating numerous and diverse cellular processes and allowing survival under a plethora of stressful conditions. JDPs are also among the most versatile chaperones. Compared to Hsp70s, the number of JDP-encoding genes has proliferated, suggesting the emergence of highly complex Hsp70-JDP networks, particularly in plants. Recent studies indicate that besides the increase in the number of JDP encoding genes; regulatory differences, neo- and sub-functionalization, and inter- and intra-class combinatorial interactions, is rapidly expanding the repertoire of Hsp70-JDP systems. This results in highly robust and functionally diverse chaperone networks in plants. Here, we review the current status of plant JDP research and discuss how the paradigm shift in the field can be exploited toward a better understanding of JDP function and evolution.
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Affiliation(s)
- Amit K. Verma
- Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, India
| | - Chetana Tamadaddi
- Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, India
| | - Yogesh Tak
- Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, India
| | - Silviya S. Lal
- Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, India
| | - Sierra J. Cole
- Department of Chemistry, Lafayette College, Easton, PA, USA
| | | | - Chandan Sahi
- Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, India
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9
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Meher PK, Sahu TK, Gahoi S, Rao AR. ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine. Front Genet 2018; 8:235. [PMID: 29379521 PMCID: PMC5770798 DOI: 10.3389/fgene.2017.00235] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 12/27/2017] [Indexed: 12/24/2022] Open
Abstract
Heat shock proteins (HSPs) play a pivotal role in cell growth and variability. Since conventional approaches are expensive and voluminous protein sequence information is available in the post-genomic era, development of an automated and accurate computational tool is highly desirable for prediction of HSPs, their families and sub-types. Thus, we propose a computational approach for reliable prediction of all these components in a single framework and with higher accuracy as well. The proposed approach achieved an overall accuracy of ~84% in predicting HSPs, ~97% in predicting six different families of HSPs, and ~94% in predicting four types of DnaJ proteins, with bench mark datasets. The developed approach also achieved higher accuracy as compared to most of the existing approaches. For easy prediction of HSPs by experimental scientists, a user friendly web server ir-HSP is made freely accessible at http://cabgrid.res.in:8080/ir-hsp. The ir-HSP was further evaluated for proteome-wide identification of HSPs by using proteome datasets of eight different species, and ~50% of the predicted HSPs in each species were found to be annotated with InterPro HSP families/domains. Thus, the developed computational method is expected to supplement the currently available approaches for prediction of HSPs, to the extent of their families and sub-types.
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Affiliation(s)
- Prabina K Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Tanmaya K Sahu
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Shachi Gahoi
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Atmakuri R Rao
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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10
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Tang H, Cao RZ, Wang W, Liu TS, Wang LM, He CM. A two-step discriminated method to identify thermophilic proteins. INT J BIOMATH 2017. [DOI: 10.1142/s1793524517500504] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Improving thermostability of an enzyme can accelerate the relevant chemical reaction. Thus, the analysis and prediction of thermophilic proteins are conducive to protein engineering and enzyme design. In this study, a novel method based on two-step discrimination was proposed to distinguish between thermophilic and non-thermophilic proteins. The model was rigorously benchmarked on an objective dataset including 915 thermophilic proteins and 793 non-thermophilic proteins. Results showed that the overall accuracy of our method is 94.44% in 5-fold cross-validation, which is higher than those of other published methods. We believe that the two-step discriminated strategy will become a promising method in the relevant field of protein bioinformatics.
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Affiliation(s)
- Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, P. R. China
| | - Ren-Zhi Cao
- Computer Science Department, Pacific Lutheran University, Tacoma WA 98447, USA
| | - Wen Wang
- Computer Science Department, Pacific Lutheran University, Tacoma WA 98447, USA
| | - Tie-Shan Liu
- Maize Institute, Shandong Academy of Agricultural Science, Jinan 250100, P. R. China
| | - Li-Ming Wang
- Maize Institute, Shandong Academy of Agricultural Science, Jinan 250100, P. R. China
| | - Chun-Mei He
- Maize Institute, Shandong Academy of Agricultural Science, Jinan 250100, P. R. China
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11
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Liu X, Wang X, Su Q, Zhang M, Zhu Y, Wang Q, Wang Q. A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:8272091. [PMID: 28127385 PMCID: PMC5239990 DOI: 10.1155/2017/8272091] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Revised: 07/12/2016] [Accepted: 08/01/2016] [Indexed: 11/17/2022]
Abstract
Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.
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Affiliation(s)
- Xiao Liu
- School of Economics and Management, Tongji University, Shanghai, China
| | - Xiaoli Wang
- School of Economics and Management, Tongji University, Shanghai, China
| | - Qiang Su
- School of Economics and Management, Tongji University, Shanghai, China
| | - Mo Zhang
- School of Economics and Management, Shanghai Maritime University, Shanghai, China
| | - Yanhong Zhu
- Department of Scientific Research, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Qiugen Wang
- Trauma Center, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Qian Wang
- Trauma Center, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
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12
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Heat Shock Proteins in Aquaculture Disease Immunology and Stress Response of Crustaceans. HEAT SHOCK PROTEINS 2017. [DOI: 10.1007/978-3-319-73377-7_10] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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13
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Predicting the Organelle Location of Noncoding RNAs Using Pseudo Nucleotide Compositions. Interdiscip Sci 2016; 9:540-544. [PMID: 27739055 DOI: 10.1007/s12539-016-0193-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 09/28/2016] [Accepted: 10/06/2016] [Indexed: 11/27/2022]
Abstract
Noncoding RNAs (ncRNAs) are implicated in various biological processes. Recent findings have demonstrated that the function of ncRNAs correlates with their provenance. Therefore, the recognition of ncRNAs from different organelle genomes will be helpful to understand their molecular functions. However, the weakness of experimental techniques limits the progress toward studying organellar ncRNAs and their functional relevance. As a complement of experiments, computational method provides an important choice to identify ncRNA in different organelles. Thus, a computational model was developed to identify ncRNAs from kinetoplast and mitochondrion organelle genomes. In this model, RNA sequences are encoded by "pseudo dinucleotide composition." It was observed by the jackknife test that the overall success rate achieved by the proposed model was 90.08 %. We hope that the proposed method will be helpful in predicting ncRNA organellar locations.
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14
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Chen W, Feng P, Ding H, Lin H. PAI: Predicting adenosine to inosine editing sites by using pseudo nucleotide compositions. Sci Rep 2016; 6:35123. [PMID: 27725762 PMCID: PMC5057124 DOI: 10.1038/srep35123] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 09/20/2016] [Indexed: 12/24/2022] Open
Abstract
The adenosine to inosine (A-to-I) editing is the most prevalent kind of RNA editing and involves in many biological processes. Accurate identification of A-to-I editing site is invaluable for better understanding its biological functions. Due to the limitations of experimental methods, in the present study, a support vector machine based-model, called PAI, is proposed to identify A-to-I editing site in D. melanogaster. In this model, RNA sequences are encoded by "pseudo dinucleotide composition" into which six RNA physiochemical properties were incorporated. PAI achieves promising performances in jackknife test and independent dataset test, indicating that it holds very high potential to become a useful tool for identifying A-to-I editing site. For the convenience of experimental scientists, a web-server was constructed for PAI and it is freely accessible at http://lin.uestc.edu.cn/server/PAI.
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Affiliation(s)
- Wei Chen
- Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, 063000, China
| | - Pengmian Feng
- School of Public Health, North China University of Science and Technology, Tangshan, 063000, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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15
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Zhao YW, Lai HY, Tang H, Chen W, Lin H. Prediction of phosphothreonine sites in human proteins by fusing different features. Sci Rep 2016; 6:34817. [PMID: 27698459 PMCID: PMC5048138 DOI: 10.1038/srep34817] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 09/20/2016] [Indexed: 01/24/2023] Open
Abstract
Phosphorylation is one of the most important protein post-translation modifications. With the rapid development of high-throughput mass spectrometry, phosphorylation site data is rapidly accumulating, which provides us an opportunity to systematically investigate and predict phosphorylation in proteins. The phosphorylation of threonine is the addition of a phosphoryl group to its polar side chains group. In this work, we statistically analyzed the distribution of the different properties including position conservation, secondary structure, accessibility and some other physicochemical properties of the residues surrounding the phosphothreonine site and non-phosphothreonine site. We found that the distributions of those features are non-symmetrical. Based on the distribution of properties, we developed a new model by using optimal window size strategy and feature selection technique. The cross-validated results show that the area under receiver operating characteristic curve reaches to 0.847, suggesting that our model may play a complementary role to other existing methods for predicting phosphothreonine site in proteins.
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Affiliation(s)
- Ya-Wei Zhao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hong-Yan Lai
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
| | - Wei Chen
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Chen W, Feng P, Tang H, Ding H, Lin H. RAMPred: identifying the N(1)-methyladenosine sites in eukaryotic transcriptomes. Sci Rep 2016; 6:31080. [PMID: 27511610 PMCID: PMC4980636 DOI: 10.1038/srep31080] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Accepted: 07/12/2016] [Indexed: 12/23/2022] Open
Abstract
N(1)-methyladenosine (m(1)A) is a prominent RNA modification involved in many biological processes. Accurate identification of m(1)A site is invaluable for better understanding the biological functions of m(1)A. However, limitations in experimental methods preclude the progress towards the identification of m(1)A site. As an excellent complement of experimental methods, a support vector machine based-method called RAMPred is proposed to identify m(1)A sites in H. sapiens, M. musculus and S. cerevisiae genomes for the first time. In this method, RNA sequences are encoded by using nucleotide chemical property and nucleotide compositions. RAMPred achieves promising performances in jackknife tests, cross cell line tests and cross species tests, indicating that RAMPred holds very high potential to become a useful tool for identifying m(1)A sites. For the convenience of experimental scientists, a web-server based on the proposed model was constructed and could be freely accessible at http://lin.uestc.edu.cn/server/RAMPred.
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Affiliation(s)
- Wei Chen
- Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, Tangshan 063000, China
| | - Pengmian Feng
- School of Public Health, North China University of Science and Technology, Tangshan, 063000, China
| | - Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics and Center for Information in Biomedicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics and Center for Information in Biomedicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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17
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Identification of apolipoprotein using feature selection technique. Sci Rep 2016; 6:30441. [PMID: 27443605 PMCID: PMC4957217 DOI: 10.1038/srep30441] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Accepted: 07/01/2016] [Indexed: 12/16/2022] Open
Abstract
Apolipoprotein is a kind of protein which can transport the lipids through the lymphatic and circulatory systems. The abnormal expression level of apolipoprotein always causes angiocardiopathy. Thus, correct recognition of apolipoprotein from proteomic data is very crucial to the comprehension of cardiovascular system and drug design. This study is to develop a computational model to predict apolipoproteins. In the model, the apolipoproteins and non-apolipoproteins were collected to form benchmark dataset. On the basis of the dataset, we extracted the g-gap dipeptide composition information from residue sequences to formulate protein samples. To exclude redundant information or noise, the analysis of various (ANOVA)-based feature selection technique was proposed to find out the best feature subset. The support vector machine (SVM) was selected as discrimination algorithm. Results show that 96.2% of sensitivity and 99.3% of specificity were achieved in five-fold cross-validation. These findings open new perspectives to improve apolipoproteins prediction by considering the specific dipeptides. We expect that these findings will help to improve drug development in anti-angiocardiopathy disease.
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18
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Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition. BIOMED RESEARCH INTERNATIONAL 2016; 2016:1654623. [PMID: 27437396 PMCID: PMC4942628 DOI: 10.1155/2016/1654623] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Accepted: 05/30/2016] [Indexed: 11/18/2022]
Abstract
Owing to the abuse of antibiotics, drug resistance of pathogenic bacteria becomes more and more serious. Therefore, it is interesting to develop a more reasonable way to solve this issue. Because they can destroy the bacterial cell structure and then kill the infectious bacterium, the bacterial cell wall lyases are suitable candidates of antibacteria sources. Thus, it is urgent to develop an accurate and efficient computational method to predict the lyases. Based on the consideration, in this paper, a set of objective and rigorous data was collected by searching through the Universal Protein Resource (the UniProt database), whereafter a feature selection technique based on the analysis of variance (ANOVA) was used to acquire optimal feature subset. Finally, the support vector machine (SVM) was used to perform prediction. The jackknife cross-validated results showed that the optimal average accuracy of 84.82% was achieved with the sensitivity of 76.47% and the specificity of 93.16%. For the convenience of other scholars, we built a free online server called Lypred. We believe that Lypred will become a practical tool for the research of cell wall lyases and development of antimicrobial agents.
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Chen W, Tang H, Lin H. MethyRNA: a web server for identification of N6-methyladenosine sites. J Biomol Struct Dyn 2016; 35:683-687. [DOI: 10.1080/07391102.2016.1157761] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Wei Chen
- Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063009, China
| | - Hua Tang
- Department of Pathophysiology, Sichuan Medical University, Luzhou 646000, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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20
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Che Y, Ju Y, Xuan P, Long R, Xing F. Identification of Multi-Functional Enzyme with Multi-Label Classifier. PLoS One 2016; 11:e0153503. [PMID: 27078147 PMCID: PMC4831692 DOI: 10.1371/journal.pone.0153503] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 03/30/2016] [Indexed: 11/23/2022] Open
Abstract
Enzymes are important and effective biological catalyst proteins participating in almost all active cell processes. Identification of multi-functional enzymes is essential in understanding the function of enzymes. Machine learning methods perform better in protein structure and function prediction than traditional biological wet experiments. Thus, in this study, we explore an efficient and effective machine learning method to categorize enzymes according to their function. Multi-functional enzymes are predicted with a special machine learning strategy, namely, multi-label classifier. Sequence features are extracted from a position-specific scoring matrix with autocross-covariance transformation. Experiment results show that the proposed method obtains an accuracy rate of 94.1% in classifying six main functional classes through five cross-validation tests and outperforms state-of-the-art methods. In addition, 91.25% accuracy is achieved in multi-functional enzyme prediction, which is often ignored in other enzyme function prediction studies. The online prediction server and datasets can be accessed from the link http://server.malab.cn/MEC/.
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Affiliation(s)
- Yuxin Che
- School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China
| | - Ying Ju
- School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China
| | - Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Ren Long
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
| | - Fei Xing
- School of Aerospace Engineering, Xiamen University, Xiamen, Fujian 361005, China
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21
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Ahmad S, Kabir M, Hayat M. Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 122:165-174. [PMID: 26233307 DOI: 10.1016/j.cmpb.2015.07.005] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Revised: 06/21/2015] [Accepted: 07/13/2015] [Indexed: 06/04/2023]
Abstract
Heat Shock Proteins (HSPs) are the substantial ingredients for cell growth and viability, which are found in all living organisms. HSPs manage the process of folding and unfolding of proteins, the quality of newly synthesized proteins and protecting cellular homeostatic processes from environmental stress. On the basis of functionality, HSPs are categorized into six major families namely: (i) HSP20 or sHSP (ii) HSP40 or J-proteins types (iii) HSP60 or GroEL/ES (iv) HSP70 (v) HSP90 and (vi) HSP100. Identification of HSPs family and sub-family through conventional approaches is expensive and laborious. It is therefore, highly desired to establish an automatic, robust and accurate computational method for prediction of HSPs quickly and reliably. Regard, a computational model is developed for the prediction of HSPs family. In this model, protein sequences are formulated using three discrete methods namely: Split Amino Acid Composition, Pseudo Amino Acid Composition, and Dipeptide Composition. Several learning algorithms are utilized to choice the best one for high throughput computational model. Leave one out test is applied to assess the performance of the proposed model. The empirical results showed that support vector machine achieved quite promising results using Dipeptide Composition feature space. The predicted outcomes of proposed model are 90.7% accuracy for HSPs dataset and 97.04% accuracy for J-protein types, which are higher than existing methods in the literature so far.
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Affiliation(s)
- Saeed Ahmad
- Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan
| | - Muhammad Kabir
- Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan.
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JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method. BIOMED RESEARCH INTERNATIONAL 2015; 2015:705156. [PMID: 26587542 PMCID: PMC4637456 DOI: 10.1155/2015/705156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 10/05/2015] [Accepted: 10/11/2015] [Indexed: 11/17/2022]
Abstract
Different types of J-proteins perform distinct functions in chaperone processes and diseases development. Accurate identification of types of J-proteins will provide significant clues to reveal the mechanism of J-proteins and contribute to developing drugs for diseases. In this study, an ensemble predictor called JPPRED for J-protein prediction is proposed with hybrid features, including split amino acid composition (SAAC), pseudo amino acid composition (PseAAC), and position specific scoring matrix (PSSM). To deal with the imbalanced benchmark dataset, the synthetic minority oversampling technique (SMOTE) and undersampling technique are applied. The average sensitivity of JPPRED based on above-mentioned individual feature spaces lies in the range of 0.744–0.851, indicating the discriminative power of these features. In addition, JPPRED yields the highest average sensitivity of 0.875 using the hybrid feature spaces of SAAC, PseAAC, and PSSM. Compared to individual base classifiers, JPPRED obtains more balanced and better performance for each type of J-proteins. To evaluate the prediction performance objectively, JPPRED is compared with previous study. Encouragingly, JPPRED obtains balanced performance for each type of J-proteins, which is significantly superior to that of the existing method. It is anticipated that JPPRED can be a potential candidate for J-protein prediction.
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23
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Survey of Programs Used to Detect Alternative Splicing Isoforms from Deep Sequencing Data In Silico. BIOMED RESEARCH INTERNATIONAL 2015; 2015:831352. [PMID: 26421304 PMCID: PMC4573434 DOI: 10.1155/2015/831352] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Revised: 02/17/2015] [Accepted: 03/02/2015] [Indexed: 11/29/2022]
Abstract
Next-generation sequencing techniques have been rapidly emerging. However, the massive sequencing reads hide a great deal of unknown important information. Advances have enabled researchers to discover alternative splicing (AS) sites and isoforms using computational approaches instead of molecular experiments. Given the importance of AS for gene expression and protein diversity in eukaryotes, detecting alternative splicing and isoforms represents a hot topic in systems biology and epigenetics research. The computational methods applied to AS prediction have improved since the emergence of next-generation sequencing. In this study, we introduce state-of-the-art research on AS and then compare the research methods and software tools available for AS based on next-generation sequencing reads. Finally, we discuss the prospects of computational methods related to AS.
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Huang Q, You Z, Zhang X, Zhou Y. Prediction of protein-protein interactions with clustered amino acids and weighted sparse representation. Int J Mol Sci 2015; 16:10855-69. [PMID: 25984606 PMCID: PMC4463679 DOI: 10.3390/ijms160510855] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Revised: 05/06/2015] [Accepted: 05/07/2015] [Indexed: 01/22/2023] Open
Abstract
With the completion of the Human Genome Project, bioscience has entered into the era of the genome and proteome. Therefore, protein–protein interactions (PPIs) research is becoming more and more important. Life activities and the protein–protein interactions are inseparable, such as DNA synthesis, gene transcription activation, protein translation, etc. Though many methods based on biological experiments and machine learning have been proposed, they all spent a long time to learn and obtained an imprecise accuracy. How to efficiently and accurately predict PPIs is still a big challenge. To take up such a challenge, we developed a new predictor by incorporating the reduced amino acid alphabet (RAAA) information into the general form of pseudo-amino acid composition (PseAAC) and with the weighted sparse representation-based classification (WSRC). The remarkable advantages of introducing the reduced amino acid alphabet is being able to avoid the notorious dimensionality disaster or overfitting problem in statistical prediction. Additionally, experiments have proven that our method achieved good performance in both a low- and high-dimensional feature space. Among all of the experiments performed on the PPIs data of Saccharomyces cerevisiae, the best one achieved 90.91% accuracy, 94.17% sensitivity, 87.22% precision and a 83.43% Matthews correlation coefficient (MCC) value. In order to evaluate the prediction ability of our method, extensive experiments are performed to compare with the state-of-the-art technique, support vector machine (SVM). The achieved results show that the proposed approach is very promising for predicting PPIs, and it can be a helpful supplement for PPIs prediction.
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Affiliation(s)
- Qiaoying Huang
- Shenzhen Graduate School, Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, Shenzhen 518055, China.
| | - Zhuhong You
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
| | - Xiaofeng Zhang
- Shenzhen Graduate School, Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, Shenzhen 518055, China.
| | - Yong Zhou
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
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