Bioluminescent Proteins Prediction with Voting Strategy

Shulin       Zhao; Ying       Ju; Xiucai       Ye; Jun       Zhang; Shuguang       Han
doi:10.2174/1574893615999200601122328
Abstract

Background: Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology. In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved.
Methods: In this study, a new bioluminescent proteins prediction method, based on a voting algorithm, is proposed. Four methods of feature extraction based on the amino acid sequence were used. 314 dimensional features in total were extracted from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, a voting algorithm was then used to build the model. To create the best performing model, the selection of base classifiers and vote counting rules are discussed.
Results and Conclusion: The proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. A previous prediction of bioluminescent proteins in three lineages was also improved using the model building method, resulting in greatly improved accuracy.
Keywords: Bioluminescent proteins, prediction, feature extraction, voting algorithm, base classifiers, vote counting rules.
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[1] 
Widder EA. Bioluminescence in the ocean: origins of biological, chemical, and ecological diversity. Science  2010; 328(5979): 704-8.
[http://dx.doi.org/10.1126/science.1174269] [PMID:  20448176] 
[2] 
Kheirabadi M, Sharafian Z, Naderi-Manesh H, Heineman U, Gohlke U, Hosseinkhani S. Crystal structure of native and a mutant of Lampyris turkestanicus luciferase implicate in bioluminescence color shift. Biochim Biophys Acta  2013; 1834(12): 2729-35.
[http://dx.doi.org/10.1016/j.bbapap.2013.09.022] [PMID:  24103420] 
[3] 
Wilson T, Hastings JW. Bioluminescence. Annu Rev Cell Dev Biol  1998; 14: 197-230.
[http://dx.doi.org/10.1146/annurev.cellbio.14.1.197] [PMID:  9891783] 
[4] 
Contag CH, Bachmann MH. Advances in in vivo bioluminescence imaging of gene expression. Annu Rev Biomed Eng  2002; 4: 235-60.
[http://dx.doi.org/10.1146/annurev.bioeng.4.111901.093336]] [PMID:  12117758] 
[5] 
Calabretta MM, Montali L, Lopreside A, Michelini E, Roda A. High-Throughput bioluminescence imaging and reporter gene assay with 3d spheroids from human cell lines. Methods Mol Biol  2020; 2081: 3-14.
[http://dx.doi.org/10.1007/978-1-4939-9940-8_1] [PMID:  31721114] 
[6] 
Zhu PF, Xu Q, Hu QH, Zhang CQ. Co-regularized unsupervised feature selection. Neurocomputing  2018; 275: 2855-63.
[http://dx.doi.org/10.1016/j.neucom.2017.11.061] 
[7] 
Zhu PF, Xu Q, Hu QH, Zhang CQ, Zhao H. Multi-label feature selection with missing labels. Pattern Recognit  2018; 74: 488-502.
[http://dx.doi.org/10.1016/j.patcog.2017.09.036] 
[8] 
Zhu PF, Zhu WC, Hu QH, Zhang CQ, Zuo WM. Subspace clustering guided unsupervised feature selection. Pattern Recognit  2017; 66: 364-74.
[http://dx.doi.org/10.1016/j.patcog.2017.01.016] 
[9] 
Kandaswamy KK, Pugalenthi G, Hazrati MK, Kalies KU, Martinetz T. BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection. BMC Bioinformatics  2011; 12: 345.
[http://dx.doi.org/10.1186/1471-2105-12-345] [PMID:  21849049] 
[10] 
Zhao X, Li J, Huang Y, Ma Z, Yin M. Prediction of bioluminescent proteins using auto covariance transformation of evolutional profiles. Int J Mol Sci  2012; 13(3): 3650-60.
[http://dx.doi.org/10.3390/ijms13033650] [PMID:  22489173] 
[11] 
Liu B, Gao X, Zhang H. BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches. Nucleic Acids Res  2019; 47(20), e127.
[http://dx.doi.org/10.1093/nar/gkz740] [PMID:  31504851] 
[12] 
Fan GL, Li QZ. Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou’s pseudo amino acid composition. J Theor Biol  2013; 334: 45-51.
[http://dx.doi.org/10.1016/j.jtbi.2013.06.003] [PMID:  23770403] 
[13] 
Huang HL. Propensity scores for prediction and characterization of bioluminescent proteins from sequences. PLoS One  2014; 9(5), e97158.
[http://dx.doi.org/10.1371/journal.pone.0097158] [PMID:  24828431] 
[14] 
Nath A, Subbiah K. Unsupervised learning assisted robust prediction of bioluminescent proteins. Comput Biol Med  2016; 68: 27-36.
[http://dx.doi.org/10.1016/j.compbiomed.2015.10.013] [PMID:  26599828] 
[15] 
Zhang J, Chai H, Yang G, Ma Z. Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme. BMC Bioinformatics  2017; 18(1): 294.
[http://dx.doi.org/10.1186/s12859-017-1709-6] [PMID:  28583090] 
[16] 
Bateman A, Martin MJ, O’Donovan C, Magrane M, Apweiler R, Alpi E, et al. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res  2015; 43(Database issue): D204-12.
[PMID: 25348405] 
[17] 
Altschul S, Madden T, Schaffer A, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res  1997; 25(17): 3389-402.IEEE Access 2019; 7:	144154-64. 
[http://dx.doi.org/ 10.1109/ACCESS.2019.2938081] 
[18] 
Zhou H, Chen C, Wang M, Ma Q, Yu B. Predicting golgi-resident protein types using conditional covariance minimization with xgboost based on multiple features fusion 
[19] 
Zhang F, Ma A, Wang Z, et al. A central edge selection based overlapping community detection algorithm for the detection of overlapping structures in protein–protein interaction networks. Molecules  2018; 23(10): 2633.
[http://dx.doi.org/10.3390/molecules23102633] 
[20] 
Nakashima H, Nishikawa K. Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J Mol Biol  1994; 238(1): 54-61.
[http://dx.doi.org/10.1006/jmbi.1994.1267] [PMID:  8145256] 
[21] 
Yang W, Zhu XJ, Huang J, Ding H, Lin H. A brief survey of machine learning methods in protein sub-Golgi localization. Curr Bioinform  2019; 14: 234-40.
[http://dx.doi.org/10.2174/1574893613666181113131415] 
[22] 
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] 
[23] 
Xiong Y, Liu J, Wei DQ. An accurate feature-based method for identifying DNA-binding residues on protein surfaces. Proteins  2011; 79(2): 509-17.
[http://dx.doi.org/10.1002/prot.22898] [PMID:  21069866] 
[24] 
Liu B. BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches. Brief Bioinform  2019; 20(4): 1280-94.
[http://dx.doi.org/10.1093/bib/bbx165] [PMID:  29272359] 
[25] 
Zhu XJ, Feng CQ, Lai HY, Chen W, Lin H. Predicting protein structural classes for low-similarity sequences by evaluating different features. Knowl Base Syst  2019; 163: 787-93.
[http://dx.doi.org/10.1016/j.knosys.2018.10.007] 
[26] 
Tan JX, Lv H, Wang F, Dao FY, Chen W, Ding H. A survey for predicting enzyme family classes using machine learning methods. Curr Drug Targets  2019; 20(5): 540-50.
[http://dx.doi.org/10.2174/1389450119666181002143355] [PMID:  30277150] 
[27] 
Xiong Y, Liu J, Zhang W, Zeng T. Prediction of heme binding residues from protein sequences with integrative sequence profiles. Proteome Sci  2012; 10(Suppl. 1): S20.
[http://dx.doi.org/10.1186/1477-5956-10-S1-S20] [PMID:  22759579] 
[28] 
Yan K, Fang X, Xu Y, Liu B. Protein fold recognition based on multi-view modeling. Bioinformatics  2019; 35(17): 2982-90.
[http://dx.doi.org/10.1093/bioinformatics/btz040] [PMID:  30668845] 
[29] 
Zou Q, Wang Z, Guan X, Liu B, Wu Y, Lin Z. An approach for identifying cytokines based on a novel ensemble classifier. BioMed Res Int  2013; 2013, 686090.
[http://dx.doi.org/10.1155/2013/686090] [PMID:  24027761] 
[30] 
Cheng JH, Yang H, Liu ML, et al. Prediction of bacteriophage proteins located in the host cell using hybrid features. Chemometr Intell Lab  2018; 180: 64-9.
[http://dx.doi.org/10.1016/j.chemolab.2018.07.006] 
[31] 
Chen Z, Zhao P, Li F, et al. iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics  2018; 34(14): 2499-502.
[http://dx.doi.org/10.1093/bioinformatics/bty140] [PMID:  29528364] 
[32] 
Tan JX, Li SH, Zhang ZM, et al. Identification of hormone binding proteins based on machine learning methods. Math Biosci Eng  2019; 16(4): 2466-80.
[http://dx.doi.org/10.3934/mbe.2019123] [PMID:  31137222] 
[33] 
Yang H, Tang H, Chen XX, et al. 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] 
[34] 
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: 230-9.
[http://dx.doi.org/10.1016/j.jtbi.2018.11.012] [PMID:  30452958] 
[35] 
Ding Y, Tang J, Guo F. Identification of drug-side effect association via multiple information integration with centered kernel alignment. Neurocomputing  2019; 325: 211-24.
[http://dx.doi.org/10.1016/j.neucom.2018.10.028] 
[36] 
Yu L, Gao L. Human pathway-based disease network . IEEE/ACM Trans Comput Biol Bioinform 2019; 16: 1240-9. 
[37] 
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] 
[38] 
Ding H, Li D. Identification of mitochondrial proteins of malaria parasite using analysis of variance. Amino Acids  2015; 47(2): 329-33.
[http://dx.doi.org/10.1007/s00726-014-1862-4] [PMID:  25385313] 
[39] 
Jiang Q, Wang G, Jin S, Li Y, Wang Y. Predicting human microRNA-disease associations based on support vector machine. Int J Data Min Bioinform  2013; 8(3): 282-93.
[http://dx.doi.org/10.1504/IJDMB.2013.056078] [PMID:  24417022] 
[40] 
Wang G, Wang Y, Teng M, Zhang D, Li L, Liu Y. Signal transducers and activators of transcription-1 (STAT1) regulates microRNA transcription in interferon gamma-stimulated HeLa cells. PLoS One  2010; 5(7), e11794.
[http://dx.doi.org/10.1371/journal.pone.0011794] [PMID:  20668688] 
[41] 
Wang G, Wang Y, Feng W, et al. Transcription factor and microRNA regulation in androgen-dependent and -independent prostate cancer cells. BMC Genomics  2008; 9(Suppl. 2): S22.
[http://dx.doi.org/10.1186/1471-2164-9-S2-S22] [PMID:  18831788] 
[42] 
Su R, Wu H, Xu B, Liu X, Wei L. Developing a multi-dose computational model for drug-induced hepatotoxicity prediction based on toxicogenomics data. IEEE/ACM Trans Comput Biol Bioinformatics  2019; 16(4): 1231-9.
[http://dx.doi.org/10.1109/TCBB.2018.2858756] [PMID:  30040651] 
[43] 
Wei L, Xing P, Su R, Shi G, Ma ZS, Zou Q. CPPred-RF: a sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency. J Proteome Res  2017; 16(5): 2044-53.
[http://dx.doi.org/10.1021/acs.jproteome.7b00019] [PMID:  28436664] 
[44] 
Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics  2018; 34(23): 4007-16.
[http://dx.doi.org/10.1093/bioinformatics/bty451] [PMID:  29868903] 
[45] 
Xu L, Liang G, Wang L, Liao C. A novel hybrid sequence-based model for identifying anticancer peptides. Genes   2018; 9(3): 158.
[http://dx.doi.org/10.3390/genes9030158] [PMID:  29534013] 
[46] 
Dou L, Li X, Ding H, Xu L, Xiang H. Is there any sequence feature in the rna pseudouridine modification prediction problem? Mol Ther Nucleic Acids  2020; 19: 293-303.
[http://dx.doi.org/10.1016/j.omtn.2019.11.014] [PMID:  31865116] 
[47] 
Zhu X, He J, Zhao S, Tao W, Xiong Y, Bi S. A comprehensive comparison and analysis of computational predictors for RNA N6-methyladenosine sites of Saccharomyces cerevisiae. Brief Funct Genomics  2019; 18(6): 367-76.
[http://dx.doi.org/10.1093/bfgp/elz018] [PMID:  31609411] 
[48] 
Chu Y, Kaushik AC, Wang X, et al. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform  2021; 22(1): 451-62.
[http://dx.doi.org/10.1093/bib/bbz152] [PMID:  31885041] 
[49] 
Xiong Y, Wang Q, Yang J, Zhu X, Wei DQ. 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] 
[50] 
Liu B, Li K. iPromoter-2L2.0: identifying promoters and their types by combining smoothing cutting window algorithm and sequence-based features. Mol Ther Nucleic Acids  2019; 18: 80-7.
[http://dx.doi.org/10.1016/j.omtn.2019.08.008] [PMID:  31536883] 
[51] 
Liu B, Chen S, Yan K, Weng F. iRO-PsekGCC: identify DNA replication origins based on Pseudo k-tuple GC Composition. Front Genet  2019; 10: 842.
[http://dx.doi.org/10.3389/fgene.2019.00842] [PMID:  31620165] 
[52] 
Wang X, Yu B, Ma A, Chen C, Liu B, Ma Q. Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique. Bioinformatics  2019; 35(14): 2395-402.
[http://dx.doi.org/10.1093/bioinformatics/bty995] [PMID:  30520961] 
[53] 
Xu H, Zeng W, Zhang D, Zeng X. MOEA/HD: a multiobjective evolutionary algorithm based on hierarchical decomposition. IEEE Trans Cybern  2019; 49(2): 517-26.
[http://dx.doi.org/10.1109/TCYB.2017.2779450] [PMID:  29990272] 
[54] 
Xu H, Zeng W, Zeng X, Yen GG. An evolutionary algorithm based on minkowski distance for many-objective optimization. IEEE Trans Cybern  2019; 49(11): 3968-79.
[http://dx.doi.org/10.1109/TCYB.2018.2856208] [PMID:  30059330] 
[55] 
Zeng X, Zhong Y, Lin W, Zou Q. Predicting disease-associated circular RNAs using deep forests combined with positive-unlabeled learning methods. Brief Bioinform  2020; 21(4): 1425-36.
[56] 
Frank E, Hall M, Trigg L, Holmes G, Witten IH. Data mining in bioinformatics using Weka. Bioinformatics  2004; 20(15): 2479-81.
[http://dx.doi.org/10.1093/bioinformatics/bth261] [PMID:  15073010] 
[57] 
Parhami B. Voting algorithms. IEEE Trans Reliab  1994; 43(4): 617-29.
[http://dx.doi.org/10.1109/24.370218] 
[58] 
Ru X, Cao P, Li L, Zou Q. Selecting essential MicroRNAs using a novel voting method. Mol Ther Nucleic Acids  2019; 18: 16-23.
[http://dx.doi.org/10.1016/j.omtn.2019.07.019] [PMID:  31479921] 
[59] 
Jamali N, Sammut C. Majority voting: material classification by tactile sensing using surface texture. IEEE Trans Robot  2011; 27(3): 508-21.
[http://dx.doi.org/10.1109/TRO.2011.2127110] 
[60] 
Kang X-B, Lin G-F, Chen Y-J, Zhao F, Zhang E-H, Jing C-N. Robust and secure zero-watermarking algorithm for color images based on majority voting pattern and hyper-chaotic encryption. Multimedia Tools Appl  2020; 79(11)
[http://dx.doi.org/10.1007/s11042-019-08191-y] 
[61] 
Rahman QA, Janmohamed T, Clarke H, Ritvo P, Heffernan J, Katz J. Interpretability and class imbalance in prediction models for pain volatility in manage my pain app users: analysis using feature selection and majority voting methods. JMIR Med Inform  2019; 7(4), e15601.
[http://dx.doi.org/10.2196/15601] [PMID:  31746764] 
[62] 
Yu L, Yao S, Gao L, Zha Y. Conserved disease modules extracted from multilayer heterogeneous disease and gene networks for understanding disease mechanisms and predicting disease treatments. Front Genet  2019; 9: 745.
[http://dx.doi.org/10.3389/fgene.2018.00745] [PMID:  30713550] 
[63] 
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): 971-82.
[http://dx.doi.org/10.7150/ijbs.23350] [PMID:  29989066] 
[64] 
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: 635-44.
[http://dx.doi.org/10.1016/j.omtn.2018.07.004] [PMID:  30081234] 
[65] 
Zeng X, Lin W, Guo M, Zou Q. A comprehensive overview and evaluation of circular RNA detection tools. PLOS Comput Biol  2017; 13(6), e1005420.
[http://dx.doi.org/10.1371/journal.pcbi.1005420] [PMID:  28594838] 
[66] 
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] 
[67] 
Liu B, Zhu Y. ProtDec-LTR3.0: Protein remote homology
	detection by incorporating profile-based features into Learning to
	Rank. IEEE Access 2019; 7: 102499-507.. 
[http://dx.doi.org/10.1109/ACCESS.2019.2929363] 
[68] 
Zeng X, Wang W, Chen C, Yen GG. A consensus community-based particle swarm optimization for dynamic community detection. IEEE Trans Cybern 2019.
[http://dx.doi.org/10.1109/TCYB.2019.2938895] [PMID:  31545758] 
[69] 
Lin X, Quan Z, Wang Z-J, Huang H, Zeng X. A novel molecular representation with BiGRU neural networks for learning atom. Brief Bioinform  2020; 21: 2099-111.
[http://dx.doi.org/10.1093/bib/bbz125] [PMID:  31729524] 
[70] 
Zeng X, Ding N. RodrA-guez-PatA3n A, Zou Q. Probability-based collaborative filtering model for predicting gene-disease associations. BMC Med Genomics  2017; 10(5): 76.
[http://dx.doi.org/10.1186/s12920-017-0313-y] [PMID:  29297351] 
[71] 
Zeng X, Liao Y, Liu Y, Zou Q. Prediction and validation of disease genes using hetesim scores. IEEE/ACM Trans Comput Biol Bioinformatics  2017; 14(3): 687-95.
[http://dx.doi.org/10.1109/TCBB.2016.2520947] [PMID:  26890920] 
[72] 
Song T. RodrA-guez-PatA3n A, Zheng P. Zeng XJIToC, Systems D. Spiking neural p systems with colored spikes. IEEE Trans Cog Develop Syst  2018; 10(4): 1106-15.
[http://dx.doi.org/10.1109/TCDS.2017.2785332] 
[73] 
Ruta D, Gabrys B. Classifier selection for majority voting. Information Fusion  2005; 6(1): 63-81.
[74] 
Yu L, Wang B, Ma X, Gao L. The extraction of drug-disease correlations based on module distance in incomplete human interactome. BMC Syst Biol  2016; 10(Suppl. 4): 111.
[http://dx.doi.org/10.1186/s12918-016-0364-2] [PMID:  28155709] 
[75] 
Jimenez LO, Morales-Morell A, Creus A. Classification of hyperdimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks. IEEE Trans Geosci Remote Sens  1999; 37(3): 1360-6.
[http://dx.doi.org/10.1109/36.763300] 
[76] 
Breiman L. Random forests. Mach Learn  2001; 45(1): 5-32.
[http://dx.doi.org/10.1023/A:1010933404324] 
[77] 
Ding Y, Tang J, Guo F. Identification of drug-target interactions via multiple information integration. Inf Sci  2017; 418-419: 546-60.
[http://dx.doi.org/10.1016/j.ins.2017.08.045] 
[78] 
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] 
[79] 
Xu L, Liang G, Liao C, Chen G-D, Chang C-C. k-Skip-n-Gram-RF: a random forest based method for Alzheimer’s disease protein identification. Front Genet  2019; 10(33): 33.
[http://dx.doi.org/10.3389/fgene.2019.00033] [PMID:  30809242] 
[80] 
Flake GW, Lawrence S. Efficient SVM regression training with SMO. Mach Learn  2002; 46(1-3): 271-90.
[http://dx.doi.org/10.1023/A:1012474916001] 
[81] 
Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol  2011; 2(3 ): 27. Article no.
[http://dx.doi.org/10.1145/1961189.1961199] 
[82] 
Chen W, Ding H, Feng P, Lin H, Chou KC. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget  2016; 7(13): 16895-909.
[http://dx.doi.org/10.18632/oncotarget.7815] [PMID:  26942877] 
[83] 
Ding Y, Tang J, Guo F. Identification of protein-protein interactions via a novel matrix-based sequence representation model with amino acid contact information. Int J Mol Sci  2016; 17(10): 1623.
[http://dx.doi.org/10.3390/ijms17101623] [PMID:  27669239] 
[84] 
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): 1269-75.
[http://dx.doi.org/10.1039/C5MB00883B] [PMID:  26883492] 
[85] 
Zhao Y, Wang F, Juan L. MicroRNA promoter identification in arabidopsis using multiple histone markers. BioMed Res Int  2015; 2015, 861402.
[http://dx.doi.org/10.1155/2015/861402] [PMID:  26425556] 
[86] 
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): 3140.
[http://dx.doi.org/10.3390/molecules23123140] [PMID:  30501121] 
[87] 
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): 1773.
[http://dx.doi.org/10.3390/ijms19061773] [PMID:  29914044] 
[88] 
Li C-C, Liu B. MotifCNN-fold: protein fold recognition based on fold-specific features extracted by motif-based convolutional neural networks. Brief Bioinform  2020; 21(6): 2133-41.
[http://dx.doi.org/10.1093/bib/bbz133] [PMID:  31774907] 
[89] 
Liu B, Li CC, Yan K. DeepSVM-fold: protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks. Brief Bioinform  2020; 21(5): 1733-41.
[http://dx.doi.org/10.1093/bib/bbz098] [PMID:  31665221] 
[90] 
Wang X, Zeng X, Ju Y, Jiang Y, Zhang Z, Chen WJCB. A classification method for microarrays based on diversity. Curr Bioinform  2016; 11(5): 590-7.
[http://dx.doi.org/10.2174/1574893609666140820224436] 
[91] 
Bonny C, Nicod P, Waeber G. IB1, a JIP-1-related nuclear protein present in insulin-secreting cells. J Biol Chem  1998; 273(4): 1843-6.
[http://dx.doi.org/10.1074/jbc.273.4.1843] [PMID:  9442013] 
[92] 
Pashaei E, Ozen M, Aydin N. Biomarker discovery based on BBHA and AdaboostM1 on microarray data for cancer classification. In: Patton J, Barbieri R, Ji J, Jabbari E, Dokos S, Mukkamala R, Eds. 38th Annual International Conference of the Ieee Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Conference Proceedings.  2016, pp. 3080-3083. 
[http://dx.doi.org/10.1109/EMBC.2016.7591380] 
[93] 
Shah M, Marchand M, Corbeil J. Feature selection with conjunctions of decision stumps and learning from microarray data. IEEE Trans Pattern Anal Mach Intell  2012; 34(1): 174-86.
[http://dx.doi.org/10.1109/TPAMI.2011.82] [PMID:  21576745] 
[94] 
Prasad AM, Iverson LR, Liaw A. Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems  2006; 9(2): 181-99.
[http://dx.doi.org/10.1007/s10021-005-0054-1] 
[95] 
Kim SB, Han KS, Rim HC, Myaeng SH. Some effective techniques for naive Bayes text classification. IEEE Trans Knowl Data Eng  2006; 18(11): 1457-66.
[http://dx.doi.org/10.1109/TKDE.2006.180] 
[96] 
Feng PM, Ding H, Chen W, Lin H. NaA_ve 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] 
[97] 
Feng PM, Lin H, Chen W. Identification of antioxidants from sequence information using naA_ve Bayes. Comput Math Methods Med  2013; 2013, 567529.
[http://dx.doi.org/10.1155/2013/567529] [PMID:  24062796] 
[98] 
Wang G, Luo X, Wang J, et al. MeDReaders: a database for transcription factors that bind to methylated DNA. Nucleic Acids Res  2018; 46(D1): D146-51.
[http://dx.doi.org/10.1093/nar/gkx1096] [PMID:  29145608] 
[99] 
Qu K, Wei L, Zou Q. A Review of DNA-binding proteins prediction methods. Curr Bioinform  2019; 14(3): 246-54.
[http://dx.doi.org/10.2174/1574893614666181212102030] 
[100] 
Zhang J, Chen Q, Liu B. DeepDRBP-2L: a new genome annotation predictor for identifying DNA binding proteins and RNA binding proteins using convolutional neural network and long short-term memory. IEEE/ACM Trans Comput Biol Bioinformatics 2019.
[http://dx.doi.org/10.1109/TCBB.2019.2952338] [PMID:  31722485] 
[101] 
Zeng X, Zhu S, Liu X, Zhou Y, Nussinov R, Cheng F. deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics  2019; 35(24): 5191-8.
[http://dx.doi.org/10.1093/bioinformatics/btz418] [PMID:  31116390] 
[102] 
Zeng X, Lin Y, He Y, Lv L, Min X, Rodriguez-Paton A. Deep collaborative filtering for prediction of disease genes. IEEE/ACM Trans Comput Biol Bioinformatics 2019.
[http://dx.doi.org/10.1109/TCBB.2019.2907536] [PMID:  30932845] 
[103] 
Cheng L, Wang P, Tian R, et al. LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Res  2019; 47(D1): D140-4.
[http://dx.doi.org/10.1093/nar/gky1051] [PMID:  30380072] 
[104] 
Yao YH, Li XH, Geng LL, Nan XY, Qi ZH, Liao B. Recent progress in long noncoding RNAs prediction. Curr Bioinform  2018; 13(4): 344-51.
[http://dx.doi.org/10.2174/1574893612666170905153933] 
[105] 
Liu Y, Zeng X, He Z, Zou Q. Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources. IEEE/ACM Trans Comput Biol Bioinformatics  2017; 14(4): 905-15.
[http://dx.doi.org/10.1109/TCBB.2016.2550432] [PMID:  27076459] 
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DOI https://dx.doi.org/10.2174/1574893615999200601122328	Print ISSN 1574-8936
Publisher Name Bentham Science Publisher	Online ISSN 2212-392X
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