Login Register Cart 0
Generic placeholder image

Protein & Peptide Letters

Editor-in-Chief

ISSN (Print): 0929-8665
ISSN (Online): 1875-5305

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

A Computational Method for the Identification of Endolysins and Autolysins

Author(s): Lei Xu, Guangmin Liang*, Baowen Chen, Xu Tan*, Huaikun Xiang and Changrui Liao

Volume 27, Issue 4, 2020

Page: [329 - 336] Pages: 8

DOI: 10.2174/0929866526666191002104735

Price: $65

Abstract

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes.

Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work.

Methods: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme.

Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set.

Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

Keywords: Cell lytic enzyme, endolysin, autolysin, tripeptides, support vector machine, computational method.

« Previous Next »
Graphical Abstract

[1]
Salazar, O.; Asenjo, J.J. Enzymatic lysis of microbial cells. Biotechnol. Lett., 2007, 29(7), 985-994.
[http://dx.doi.org/10.1007/s10529-007-9345-2] [PMID: 17464453]
[2]
Denis, G. Phage lytic enzyme Cpl-1 for antibacterial therapy in experimental Pneumococcal meningitis. J. Infect. Dis., 2008, 197(11), 1519-1522.
[3]
Borysowski, J.; Weber-Dabrowska, B.; Górski, A. Bacteriophage endolysins as a novel class of antibacterial agents. Exp. Biol. Med. (Maywood), 2006, 231(4), 366-377.
[http://dx.doi.org/10.1177/153537020623100402] [PMID: 16565432]
[4]
Koch, A.L. Autolysis control hypotheses for tolerance to wall antibiotics. Antimicrob. Agents Chemother., 2001, 45(10), 2671-2675.
[http://dx.doi.org/10.1128/AAC.45.10.2671-2675.2001] [PMID: 11557453]
[5]
Fischetti, V.A. Bacteriophage lytic enzymes: Novel anti-infectives. Trends Microbiol., 2005, 13(10), 491-496.
[http://dx.doi.org/10.1016/j.tim.2005.08.007]
[6]
Fu, J.; Tang, J.; Wang, Y.; Cui, X.; Yang, Q.; Hong, J.; Li, X.; Li, S.; Chen, Y.; Xue, W.; Zhu, F. Discovery of the consistently well-performed analysis chain for SWATH-MS based pharmaco-proteomic quantification. Front. Pharmacol., 2018, 9, 681.
[http://dx.doi.org/10.3389/fphar.2018.00681] [PMID: 29997509]
[7]
Raymond, S.; Daniel, N.; Fischetti, V.A. A bacteriolytic agent that detects and kills Bacillus anthracis. Nature, 2002, 418(6900), 884-889.
[http://dx.doi.org/10.1038/nature01026]
[8]
Nelson, D.; Loomis, L.; Fischetti, V.A. Prevention and elimination of upper respiratory colonization of mice by group A Streptococci by using a bacteriophage lytic enzyme. Proc. Natl. Acad. Sci. USA, 2001, 98(7), 4107-4112.
[http://dx.doi.org/10.1073/pnas.061038398] [PMID: 11259652]
[9]
Cui, X.; Yang, Q.; Li, B.; Tang, J.; Zhang, X.; Li, S.; Li, F.; Hu, J.; Lou, Y.; Qiu, Y.; Xue, W.; Zhu, F. Assessing the effectiveness of direct data merging strategy in long-term and large-scale pharmacometabonomics. Front. Pharmacol., 2019, 10, 127.
[http://dx.doi.org/10.3389/fphar.2019.00127] [PMID: 30842738]
[10]
Zhu, F.; Li, X.X.; Yang, S.Y.; Chen, Y.Z. Clinical success of drug targets prospectively predicted by in silico study. Trends Pharmacol. Sci., 2018, 39(3), 229-231.
[http://dx.doi.org/10.1016/j.tips.2017.12.002] [PMID: 29295742]
[11]
Cheng, L.; Hu, Y.; Sun, J.; Zhou, M.; Jiang, Q. DincRNA: A comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function. Bioinformatics, 2018, 34(11), 1953-1956.
[http://dx.doi.org/10.1093/bioinformatics/bty002] [PMID: 29365045]
[12]
Cheng, L.; Jiang, Y.; Ju, H.; Sun, J.; Peng, J.; Zhou, M.; Hu, Y. InfAcrOnt: Calculating cross-ontology term similarities using information flow by a random walk. BMC Genomics, 2018, 19(Suppl. 1), 919.
[http://dx.doi.org/10.1186/s12864-017-4338-6] [PMID: 29363423]
[13]
Yu, L.; Zhao, J.; Gao, L. Predicting potential drugs for breast cancer based on miRNA and tissue specificity. Int. J. Biol. Sci., 2018, 14(8), 971-982.
[http://dx.doi.org/10.7150/ijbs.23350] [PMID: 29989066]
[14]
Yu, L.; Zhao, J.; Gao, L. Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome. Artif. Intell. Med., 2017, 77, 53-63.
[http://dx.doi.org/10.1016/j.artmed.2017.03.009] [PMID: 28545612]
[15]
Chou, K.C.; Zhang, C.T. Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol., 1995, 30(4), 275-349.
[http://dx.doi.org/10.3109/10409239509083488] [PMID: 7587280]
[16]
Guo, F.; Li, S.C.; Du, P.; Wang, L. Probabilistic models for capturing more physicochemical properties on protein-protein interface. J. Chem. Inf. Model., 2014, 54(6), 1798-1809.
[http://dx.doi.org/10.1021/ci5002372] [PMID: 24881460]
[17]
Guo, F.; Li, S.C.; Wang, L. Protein-protein binding sites prediction by 3D structural similarities. J. Chem. Inf. Model., 2011, 51(12), 3287-3294.
[http://dx.doi.org/10.1021/ci200206n] [PMID: 22077765]
[18]
Guo, F.; Li, S.C.; Ma, W.; Wang, L. Detecting protein conformational changes in interactions via scaling known structures. J. Comput. Biol., 2013, 20(10), 765-779.
[http://dx.doi.org/10.1089/cmb.2013.0069] [PMID: 24093228]
[19]
Liu, B. BioSeq-Analysis: A platform for DNA, RNA and protein sequence analysis based on machine learning approaches. Brief. Bioinform., 2019, 20(4), 1280-1294.
[http://dx.doi.org/10.1093/bib/bbx165] [PMID: 29272359]
[20]
Zhu, X.J. Predicting protein structural classes for low-similarity sequences by evaluating different features. Knowl. Base. Syst., 2019, 163, 787-793.
[http://dx.doi.org/10.1016/j.knosys.2018.10.007]
[21]
Shen, H.B.; Chou, K.C. EzyPred: A top-down approach for predicting enzyme functional classes and subclasses. Biochem. Biophys. Res. Commun., 2007, 364(1), 53-59.
[http://dx.doi.org/10.1016/j.bbrc.2007.09.098] [PMID: 17931599]
[22]
Liu, B.; Wang, X.; Zou, Q.; Dong, Q.; Chen, Q. protein remote homology detection by combining chou’s pseudo amino acid composition and profile-based protein representation. Mol. Inform., 2013, 32(9-10), 775-782.
[http://dx.doi.org/10.1002/minf.201300084] [PMID: 27480230]
[23]
Cheng, X-Y.; Huang, W.J.; Hu, S.C.; Zhang, H.L.; Wang, H.; Zhang, J.X.; Lin, H.H.; Chen, Y.Z.; Zou, Q.; Ji, Z.L. A global characterization and identification of multifunctional enzymes. PLoS One, 2012, 7(6) e38979
[http://dx.doi.org/10.1371/journal.pone.0038979] [PMID: 22723914]
[24]
Li, Y.H.; Li, X.X.; Hong, J.J.; Wang, Y.X.; Fu, J.B.; Yang, H.; Yu, C.Y.; Li, F.C.; Hu, J.; Xue, W.W.; Jiang, Y.Y.; Chen, Y.Z.; Zhu, F. Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs. Brief. Bioinform., 2019. Epub ahead of print
[http://dx.doi.org/10.1093/bib/bby130] [PMID: 30689717]
[25]
Xu, L.; Liang, G.; Shi, S.; Liao, C. SeqSVM: A sequence-based support vector machine method for identifying antioxidant proteins. Int. J. Mol. Sci., 2018, 19(6) E1773
[http://dx.doi.org/10.3390/ijms19061773]
[26]
Xu, L.; Liang, G.; Wang, L.; Lio, C. A novel hybrid sequence-based model for identifying anticancer peptides. Genes, 2018, 9(3), 158.
[http://dx.doi.org/10.3390/genes9030158]
[27]
Li, Y.; Niu, M.; Zou, Q. ELM-MHC: An improved MHC identification method with extreme learning machine algorithm. J. Proteome Res., 2019, 18(3), 1392-1401.
[http://dx.doi.org/10.1021/acs.jproteome.9b00012] [PMID: 30698979]
[28]
Tang, J.; Fu, J.; Wang, Y.; Luo, Y.; Yang, Q.; Li, B.; Tu, G.; Hong, J.; Cui, X.; Chen, Y.; Yao, L.; Xue, W.; Zhu, F. Simultaneous improvement in the precision, accuracy and robustness of label-free proteome quantification by optimizing data manipulation chains. Mol. Cell. Proteomics, 2019, 18(8), 1683-1699.
[http://dx.doi.org/10.1074/mcp.RA118.001169] [PMID: 31097671]
[29]
Tang, J.; Fu, J.; Wang, Y.; Li, B.; Li, Y.; Yang, Q.; Cui, X.; Hong, J.; Li, X.; Chen, Y.; Xue, W.; Zhu, F. ANPELA: Analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Brief. Bioinform., 2019. [Epub ahead of print]
[http://dx.doi.org/10.1093/bib/bby127] [PMID: 30649171]
[30]
Wang, Y.; Ding, Y.; Guo, F.; Wei, L.; Tang, J. Improved detection of DNA-binding proteins via compression technology on PSSM information. PLoS One, 2017, 12(9)e0185587
[http://dx.doi.org/10.1371/journal.pone.0185587] [PMID: 28961273]
[31]
Xiong, Y.; Wang, Q.; Yang, J.; Zhu, X.; Wei, D.Q. PredT4SE-Stack: Prediction of bacterial type IV secreted effectors from protein sequences using a stacked ensemble method. Front. Microbiol., 2018, 9, 2571.
[http://dx.doi.org/10.3389/fmicb.2018.02571] [PMID: 30416498]
[32]
Chen, J.; Guo, M.; Li, S.; Liu, B. ProtDec-LTR2.0: An improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank. Bioinformatics, 2017, 33(21), 3473-3476.
[http://dx.doi.org/10.1093/bioinformatics/btx429] [PMID: 29077805]
[33]
Wei, L.; Chen, H.; Su, R. M6APred-EL: A sequence-based predictor for identifying n6-methyladenosine sites using ensemble learning. Mol. Ther. Nucleic Acids, 2018, 12, 635-644.
[http://dx.doi.org/10.1016/j.omtn.2018.07.004] [PMID: 30081234]
[34]
Wei, L.; Xing, P.; Zeng, J.; Chen, J.; Su, R.; Guo, F. Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier. Artif. Intell. Med., 2017, 83, 67-74.
[http://dx.doi.org/10.1016/j.artmed.2017.03.001] [PMID: 28320624]
[35]
Wei, L.; Wan, S.; Guo, J.; Wong, K.K. A novel hierarchical selective ensemble classifier with bioinformatics application. Artif. Intell. Med., 2017, 83, 82-90.
[http://dx.doi.org/10.1016/j.artmed.2017.02.005] [PMID: 28245947]
[36]
Tan, J.X.; Li, S.H.; Zhang, Z.M.; Chen, C.X.; Chen, W.; Tang, H.; Lin, H. Identification of hormone binding proteins based on machine learning methods. Math. Biosci. Eng., 2019, 16(4), 2466-2480.
[http://dx.doi.org/10.3934/mbe.2019123] [PMID: 31137222]
[37]
Cheng, L.; Hu, Y. Human disease system biology. Curr. Gene Ther., 2018, 18(5), 255-256.
[http://dx.doi.org/10.2174/1566523218666181010101114] [PMID: 30306867]
[38]
Cheng, L.; Wang, P.; Tian, R.; Wang, S.; Guo, Q.; Luo, M.; Zhou, W.; Liu, G.; Jiang, H.; Jiang, Q. LncRNA2Target v2.0: A comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Res., 2019, 47(D1), D140-D144.
[http://dx.doi.org/10.1093/nar/gky1051] [PMID: 30380072]
[39]
Chen, X.X.; Tang, H.; Li, W.C.; Wu, H.; Chen, W.; Ding, H.; Lin, H. Identification of bacterial cell wall lyases via pseudo amino acid composition. BioMed Res. Int., 2016, 20161654623
[http://dx.doi.org/10.1155/2016/1654623] [PMID: 27437396]
[40]
Ding, H.; Luo, L.; Lin, H. Prediction of cell wall lytic enzymes using Chou’s amphiphilic pseudo amino acid composition. Protein Pept. Lett., 2009, 16(4), 351-355.
[http://dx.doi.org/10.2174/092986609787848045] [PMID: 19356130]
[41]
Chou, K-C. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins, 2001, 43, 246-255.
[http://dx.doi.org/10.1002/prot.1035]
[42]
Lin, H.; Li, Q.Z. Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components. J. Comput. Chem., 2007, 28(9), 1463-1466.
[http://dx.doi.org/10.1002/jcc.20554] [PMID: 17330882]
[43]
Xue, W.; Yang, F.; Wang, P.; Zheng, G.; Chen, Y.; Yao, X.; Zhu, F. What Contributes to serotonin-norepinephrine reuptake inhibitors’ dual-targeting mechanism? the key role of transmembrane domain 6 in human serotonin and norepinephrine transporters revealed by molecular dynamics simulation. ACS Chem. Neurosci., 2018, 9(5), 1128-1140.
[http://dx.doi.org/10.1021/acschemneuro.7b00490] [PMID: 29300091]
[44]
Li, B.; Tang, J.; Yang, Q.; Li, S.; Cui, X.; Li, Y.; Chen, Y.; Xue, W.; Li, X.; Zhu, F. NOREVA: Normalization and evaluation of MS-based metabolomics data. Nucleic Acids Res., 2017, 45(W1), W162-W170.
[http://dx.doi.org/10.1093/nar/gkx449] [PMID: 28525573]
[45]
Shen, Y.; Tang, J.; Guo, F. Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC. J. Theor. Biol., 2019, 462, 230-239.
[http://dx.doi.org/10.1016/j.jtbi.2018.11.012] [PMID: 30452958]
[46]
Yang, H.; Tang, H.; Chen, X.X.; Zhang, C.J.; Zhu, P.P.; Ding, H.; Chen, W.; Lin, H. Identification of secretory proteins in Mycobacterium tuberculosis using pseudo amino acid composition. BioMed Res. Int., 2016, 2016 5413903
[http://dx.doi.org/10.1155/2016/5413903] [PMID: 27597968]
[47]
Tang, H.; Chen, W.; Lin, H. Identification of immunoglobulins using Chou’s pseudo amino acid composition with feature selection technique. Mol. Biosyst., 2016, 12(4), 1269-1275.
[http://dx.doi.org/10.1039/C5MB00883B] [PMID: 26883492]
[48]
Zhu, P.P.; Li, W.C.; Zhong, Z.J.; Deng, E.Z.; Ding, H.; Chen, W.; Lin, H. Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition. Mol. Biosyst., 2015, 11(2), 558-563.
[http://dx.doi.org/10.1039/C4MB00645C] [PMID: 25437899]
[49]
Bairoch, A.; Apweiler, R.; Wu, C.H.; Barker, W.C.; Boeckmann, B.; Ferro, S.; Gasteiger, E.; Huang, H.; Lopez, R.; Magrane, M.; Martin, M.J.; Natale, D.A.; O’Donovan, C.; Redaschi, N.; Yeh, L.S. Universal Protein Resource (UniProt). Nucleic Acids Res., 2005, 33, D154-D159.
[http://dx.doi.org/10.1093/nar/gki070] [PMID: 15608167]
[50]
Li, W.; Godzik, A.J.B. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 2006, 22(13), 1658-1659.
[http://dx.doi.org/10.1093/bioinformatics/btl158]
[51]
Zou, Q.; Lin, G.; Jiang, X.; Liu, X.; Zeng, X. Sequence clustering in bioinformatics: An empirical study. Brief. Bioinform., 2019. Epub ahead of print
[http://dx.doi.org/10.1093/bib/bby090]
[52]
Feng, C.Q. iTerm-PseKNC: A sequence-based tool for predicting bacterial transcriptional terminators. Bioinformatics, 2019, 35(9), 1469-1477.
[http://dx.doi.org/10.1093/bioinformatics/bty827] [PMID: 30247625]
[53]
Ding, H.; Li, D. Identification of mitochondrial proteins of malaria parasite using analysis of variance. Amino Acids, 2015, 47(2), 329-333.
[http://dx.doi.org/10.1007/s00726-014-1862-4] [PMID: 25385313]
[54]
Zou, Q. A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing, 2016, 173, 346-354.
[http://dx.doi.org/10.1016/j.neucom.2014.12.123]
[55]
He, J.; Fang, T.; Zhang, Z.; Huang, B.; Zhu, X.; Xiong, Y.; Pse, U.I. Pseudouridine sites identification based on RNA sequence information. BMC Bioinformatics, 2018, 19(1), 306.
[http://dx.doi.org/10.1186/s12859-018-2321-0] [PMID: 30157750]
[56]
Qiao, Y.; Xiong, Y.; Gao, H.; Zhu, X.; Chen, P. Protein-protein interface hot spots prediction based on a hybrid feature selection strategy. BMC Bioinformatics, 2018, 19(1), 14.
[http://dx.doi.org/10.1186/s12859-018-2009-5] [PMID: 29334889]
[57]
Liu, B.; Jiang, S.; Zou, Q. HITS-PR-HHblits: Protein remote homology detection by combining PageRank and Hyperlink-Induced Topic Search. Brief. Bioinform., 2018. [Epub ahead of print]
[http://dx.doi.org/10.1093/bib/bby104] [PMID: 30403770]
[58]
Wu, Y.; Chen, P.; Yao, Y.; Ye, X.; Xiao, Y.; Liao, L.; Wu, M.; Chen, J. Dysphonic voice pattern analysis of patients in Parkinson’s disease using minimum inter-class probability risk feature selection and Bagging ensemble learning methods. Comput. Math. Methods Med., 2017, 2017 4201984
[http://dx.doi.org/10.1155/2017/4201984] [PMID: 28553366]
[59]
Yu, L.; Huang, J.; Ma, Z.; Zhang, J.; Zou, Y.; Gao, L. Inferring drug-disease associations based on known protein complexes. BMC Med. Genomics, 2015, 8(Suppl. 2), S2.
[http://dx.doi.org/10.1186/1755-8794-8-S2-S2] [PMID: 26044949]
[60]
Zou, Q.; Wan, S.; Ju, Y.; Tang, J.; Zeng, X. Pretata: Predicting TATA binding proteins with novel features and dimensionality reduction strategy. BMC Syst. Biol., 2016, 10(4), 114.
[http://dx.doi.org/10.1186/s12918-016-0353-5] [PMID: 28155714]
[61]
Yang, W. A brief survey of machine learning methods in protein sub-Golgi localization. Curr. Bioinform., 2019, 14, 234-240.
[http://dx.doi.org/10.2174/1574893613666181113131415]
[62]
Yang, H.; Qiu, W.R.; Liu, G.; Guo, F.B.; Chen, W.; Chou, K.C.; Lin, H. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int. J. Biol. Sci., 2018, 14(8), 883-891.
[http://dx.doi.org/10.7150/ijbs.24616] [PMID: 29989083]
[63]
Su, Z.D.; Huang, Y.; Zhang, Z.Y.; Zhao, Y.W.; Wang, D.; Chen, W.; Chou, K.C.; Lin, H. iLoc-lncRNA: Predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics, 2018, 34(24), 4196-4204.
[http://dx.doi.org/10.1093/bioinformatics/bty508] [PMID: 29931187]
[64]
Yang, H.; Lv, H.; Ding, H.; Chen, W.; Lin, H. iRNA-2OM: A sequence-based predictor for identifying 2′-O-Methylation Sites in Homo sapiens. J. Comput. Biol., 2018, 25(11), 1266-1277.
[http://dx.doi.org/10.1089/cmb.2018.0004] [PMID: 30113871]
[65]
Tang, H.; Zhao, Y.W.; Zou, P.; Zhang, C.M.; Chen, R.; Huang, P.; Lin, H. HBPred: A tool to identify growth hormone-binding proteins. Int. J. Biol. Sci., 2018, 14(8), 957-964.
[http://dx.doi.org/10.7150/ijbs.24174] [PMID: 29989085]
[66]
Dao, F.Y. Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique. Bioinformatics, 2019, 35(12), 2075-2083.
[http://dx.doi.org/10.1093/bioinformatics/bty943] [PMID: 30428009]
[67]
Feng, P.; Ding, H.; Lin, H.; Chen, W. AOD: The antioxidant protein database. Sci. Rep., 2017, 7(1), 7449.
[http://dx.doi.org/10.1038/s41598-017-08115-6] [PMID: 28784999]
[68]
Chen, W. Recent advances in machine learning methods for predicting heat shock proteins. Curr. Drug Metab., 2018, 20(3), 224-228.
[http://dx.doi.org/10.2174/1389200219666181031105916] [PMID: 30378494]
[69]
Xu, L.; Liang, G.; Liao, C.; Chen, G.D.; Chang, C.C. An efficient classifier for Alzheimer’s disease genes identification. Molecules, 2018, 23(12) e3140
[http://dx.doi.org/10.3390/molecules23123140]
[70]
Wu, Y.; Chen, P.; Luo, X.; Huang, H.; Liao, L.; Yao, Y.; Wu, M.; Rangayyan, R.M. Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures. Comput. Methods Programs Biomed., 2016, 130, 1-12.
[http://dx.doi.org/10.1016/j.cmpb.2016.03.021] [PMID: 27208516]
[71]
Chou, K.C.; Shen, H.B. ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem. Biophys. Res. Commun., 2008, 376(2), 321-325.
[http://dx.doi.org/10.1016/j.bbrc.2008.08.125]
[72]
Chen, W. iACP: A sequence-based tool for identifying anticancer peptides. Oncotarget, 2016, 7(13), 16895-16909.
[http://dx.doi.org/10.18632/oncotarget.7815]
[73]
Zhang, N. Discriminating Ramos and Jurkat cells with image textures from diffraction imaging flow cytometry based on a support vector machine. Curr. Bioinform., 2018, 13(1), 50-56.
[http://dx.doi.org/10.2174/1574893611666160608102537]
[74]
Wang, S.P. Analysis and prediction of nitrated tyrosine sites with the mRMR method and support vector machine algorithm. Curr. Bioinform., 2018, 13(1), 3-13.
[http://dx.doi.org/10.2174/1574893611666160608075753]
[75]
Li, D.; Ju, Y.; Zou, Q. Protein folds prediction with hierarchical structured SVM. Curr. Proteomics, 2016, 13(2), 79-85.
[http://dx.doi.org/10.2174/157016461302160514000940]
[76]
Ding, Y.; Tang, J.; Guo, F. Identification of drug-target interactions via multiple information integration. Inf. Sci., 2017, 418-419, 546-560.
[http://dx.doi.org/10.1016/j.ins.2017.08.045]
[77]
Liu, B.; Yang, F.; Huang, D.S.; Chou, K.C. iPromoter-2L: A two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics, 2018, 34(1), 33-40.
[http://dx.doi.org/10.1093/bioinformatics/btx579] [PMID: 28968797]
[78]
Wu, Y.; Krishnan, S. Combining least-squares support vector machines for classification of biomedical signals: A case study with knee-joint vibroarthrographic signals. J. Exp. Theor. Artif. Intell., 2011, 23(1), 63-77.
[http://dx.doi.org/10.1080/0952813X.2010.506288]
[79]
Lai, H.Y.; Chen, X.X.; Chen, W.; Tang, H.; Lin, H. Sequence-based predictive modeling to identify cancerlectins. Oncotarget, 2017, 8(17), 28169-28175.
[http://dx.doi.org/10.18632/oncotarget.15963] [PMID: 28423655]
[80]
Ding, H.; Deng, E.Z.; Yuan, L.F.; Liu, L.; Lin, H.; Chen, W.; Chou, K.C. iCTX-type: A sequence-based predictor for identifying the types of conotoxins in targeting ion channels. BioMed Res. Int., 2014, 2014 286419
[http://dx.doi.org/10.1155/2014/286419] [PMID: 24991545]
[81]
Chen, W.; Lv, H.; Nie, F.; Lin, H. i6mA-Pred: Identifying DNA N6-methyladenine sites in the rice genome. Bioinformatics, 2019, 35(16), 2796-2800.
[http://dx.doi.org/10.1093/bioinformatics/btz015] [PMID: 30624619]
[82]
Feng, P-M.; Chen, W.; Lin, H.; Chou, K.C. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem., 2013, 442(1), 118-125.
[http://dx.doi.org/10.1016/j.ab.2013.05.024] [PMID: 23756733]
[83]
Chou, K.C.; Cai, Y.D. Using functional domain composition and support vector machines for prediction of protein subcellular location. J. Biol. Chem., 2002, 277(48), 45765-45769.
[http://dx.doi.org/10.1074/jbc.M204161200] [PMID: 12186861]
[84]
Chang, C.C.; Lin, C.J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2011, 2(3), 1-27.
[http://dx.doi.org/10.1145/1961189.1961199]
[85]
Lin, H. The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou’s pseudo amino acid composition. J. Theor. Biol., 2008, 252(2), 350-356.
[http://dx.doi.org/10.1016/j.jtbi.2008.02.004] [PMID: 18355838]
[86]
Lv, H.; Zhang, Z.M.; Li, S.H.; Tan, J.X.; Chen, W.; Lin, H. Evaluation of different computational methods on 5-methylcytosine sites identification. Brief. Bioinform., 2019. [Epub ahead of print]
[http://dx.doi.org/10.1093/bib/bbz048] [PMID: 31157855]
[87]
Feng, P.M.; Lin, H.; Chen, W. Identification of antioxidants from sequence information using naïve Bayes. Comput. Math. Methods Med., 2013, 2013 567529
[http://dx.doi.org/10.1155/2013/567529] [PMID: 24062796]
[88]
Feng, P.M.; Ding, H.; Chen, W.; Lin, H. Naïve Bayes classifier with feature selection to identify phage virion proteins. Comput. Math. Methods Med., 2013, 2013 530696
[http://dx.doi.org/10.1155/2013/530696] [PMID: 23762187]
[89]
Lin, C. LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy. Neurocomputing, 2014, 123, 424-435.
[http://dx.doi.org/10.1016/j.neucom.2013.08.004]

Rights & Permissions Print Cite
Article Metrics
30
© 2024 Bentham Science Publishers | Privacy Policy