RDR100: A Robust Computational Method for Identification of Krüppel-like Factors

  • Authors: Malik A.1, Kamli M.2, Sabir J.3, Phan L.T.4, Kim C.5, Manavalan B.6
  • Affiliations:
    1. Institute of Intelligence Informatics Technology, Sangmyung University
    2. Department of Biological Sciences, Faculty of Science, King Abdulaziz University
    3. Center of Excellence in Bionanoscience Research, King Abdulaziz University
    4. Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering,, Sungkyunkwan University
    5. Department of Biotechnology, Sangmyung University
    6. Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University
  • Issue: Vol 19, No 6 (2024)
  • Pages: 584-599
  • Section: Life Sciences
  • URL: https://jdigitaldiagnostics.com/1574-8936/article/view/643964
  • DOI: https://doi.org/10.2174/1574893618666230905102407
  • ID: 643964

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Abstract

Background:Krüppel-like factors (KLFs) are a family of transcription factors containing zinc fingers that regulate various cellular processes. KLF proteins are associated with human diseases, such as cancer, cardiovascular diseases, and metabolic disorders. The KLF family consists of 18 members with diverse expression profiles across numerous tissues. Accurate identification and annotation of KLF proteins is crucial, given their involvement in important biological functions. Although experimental approaches can identify KLF proteins precisely, large-scale identification is complicated, slow, and expensive.

Methods:In this study, we developed RDR100, a novel random forest (RF)-based framework for predicting KLF proteins based on their primary sequences. First, we identified the optimal encodings for ten different features using a recursive feature elimination approach, and then trained their respective model using five distinct machine learning (ML) classifiers.

Results:The performance of all models was assessed using independent datasets, and RDR100 was selected as the final model based on its consistent performance in cross-validation and independent evaluation.

Conclusion:Our results demonstrate that RDR100 is a robust predictor of KLF proteins. RDR100 web server is available at https://procarb.org/RDR100/.

About the authors

Adeel Malik

Institute of Intelligence Informatics Technology, Sangmyung University

Email: info@benthamscience.net

Majid Kamli

Department of Biological Sciences, Faculty of Science, King Abdulaziz University

Email: info@benthamscience.net

Jamal Sabir

Center of Excellence in Bionanoscience Research, King Abdulaziz University

Email: info@benthamscience.net

Le Thi Phan

Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering,, Sungkyunkwan University

Email: info@benthamscience.net

Chang-Bae Kim

Department of Biotechnology, Sangmyung University

Author for correspondence.
Email: info@benthamscience.net

Balachandran Manavalan

Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University

Author for correspondence.
Email: info@benthamscience.net

References

  1. Ilsley MD, Gillinder KR, Magor GW, et al. Krüppel-like factors compete for promoters and enhancers to fine-tune transcription. Nucleic Acids Res 2017; 45(11): 6572-88. doi: 10.1093/nar/gkx441 PMID: 28541545
  2. McConnell BB, Yang VW. Mammalian Krüppel-like factors in health and diseases. Physiol Rev 2010; 90(4): 1337-81. doi: 10.1152/physrev.00058.2009 PMID: 20959618
  3. Zhang Y, Yao C, Ju Z, et al. Krüppel-like factors in tumors: Key regulators and therapeutic avenues. Front Oncol 2023; 13: 1080720. doi: 10.3389/fonc.2023.1080720 PMID: 36761967
  4. Tetreault MP, Yang Y, Katz JP. Krüppel-like factors in cancer. Nat Rev Cancer 2013; 13(10): 701-13. doi: 10.1038/nrc3582 PMID: 24060862
  5. Pollak NM, Hoffman M, Goldberg IJ, Drosatos K. Krüppel-Like Factors. JACC Basic Transl Sci 2018; 3(1): 132-56. doi: 10.1016/j.jacbts.2017.09.001 PMID: 29876529
  6. Oishi Y, Manabe I. Krüppel-like factors in metabolic homeostasis and cardiometabolic disease. Front Cardiovasc Med 2018; 5: 69. doi: 10.3389/fcvm.2018.00069 PMID: 29942807
  7. Tian H, Qiao S, Zhao Y, et al. Krüppel-like transcription factor 7 is a causal gene in autism development. Int J Mol Sci 2022; 23(6): 3376. doi: 10.3390/ijms23063376 PMID: 35328799
  8. Yang M, Guo Q, Peng H, et al. Krüppel-like factor 3 inhibition by mutated lncRNA Reg1cp results in human high bone mass syndrome. J Exp Med 2019; 216(8): 1944-64. doi: 10.1084/jem.20181554 PMID: 31196982
  9. Shao M, Ge GZ, Liu WJ, et al. Characterization and phylogenetic analysis of Krüppel-like transcription factor (KLF) gene family in tree shrews (Tupaia belangeri chinensis). Oncotarget 2017; 8(10): 16325-39. doi: 10.18632/oncotarget.13883 PMID: 28032601
  10. Bernhardt C, Sock E, Fröb F, Hillgärtner S, Nemer M, Wegner M. KLF9 and KLF13 transcription factors boost myelin gene expression in oligodendrocytes as partners of SOX10 and MYRF. Nucleic Acids Res 2022; 50(20): 11509-28. doi: 10.1093/nar/gkac953 PMID: 36318265
  11. Paranjapye A, NandyMazumdar M, Harris A. Kruppel-like factor 5 regulates CFTR expression through repression by maintaining chromatin architecture coupled with direct enhancer activation. J Mol Biol 2022; 434.
  12. Cao Z, Sun X, Icli B, Wara AK, Feinberg MW. Role of Krüppel-like factors in leukocyte development, function, and disease. Blood 2010; 116(22): 4404-14. doi: 10.1182/blood-2010-05-285353 PMID: 20616217
  13. Preiss A, Rosenberg UB, Kienlin A, Seifert E, Jäckle H. Molecular genetics of Krüppel, a gene required for segmentation of the Drosophila embryo. Nature 1985; 313(5997): 27-32. doi: 10.1038/313027a0 PMID: 3917552
  14. Brayer KJ, Segal DJ. Keep your fingers off my DNA: Protein-protein interactions mediated by C2H2 zinc finger domains. Cell Biochem Biophys 2008; 50(3): 111-31. doi: 10.1007/s12013-008-9008-5 PMID: 18253864
  15. Kadonaga JT, Carner KR, Masiarz FR, Tjian R. Isolation of cDNA encoding transcription factor Sp1 and functional analysis of the DNA binding domain. Cell 1987; 51(6): 1079-90. doi: 10.1016/0092-8674(87)90594-0 PMID: 3319186
  16. Kaczynski J, Cook T, Urrutia R. Sp1- and Krüppel-like transcription factors. Genome Biol 2003; 4(2): 206. doi: 10.1186/gb-2003-4-2-206 PMID: 12620113
  17. Chang Z, Li H. KLF9 deficiency protects the heart from inflammatory injury triggered by myocardial infarction. Korean J Physiol Pharmacol 2023; 27(2): 177-85. doi: 10.4196/kjpp.2023.27.2.177 PMID: 36815257
  18. Zhou X, Kang Y, Chang Y, et al. CRC therapy identifies indian hedgehog signaling in mouse endometrial epithelial cells and inhibition of Ihh-KLF9 as a novel strategy for treating IUA. Cells 2022; 11(24): 4053. doi: 10.3390/cells11244053 PMID: 36552817
  19. Pernaa N, Keskitalo S, Chowdhury I, et al. Heterozygous premature termination in zinc-finger domain of Krüppel-like factor 2 gene associates with dysregulated immunity. Front Immunol 2022; 13: 819929. doi: 10.3389/fimmu.2022.819929 PMID: 36466816
  20. Zhou C, Sun P, Hamblin MH, Yin KJ. Genetic deletion of Krüppel-like factor 11 aggravates traumatic brain injury. J Neuroinflammation 2022; 19(1): 281. doi: 10.1186/s12974-022-02638-0 PMID: 36403074
  21. Chen Z, Lei T, Chen X, et al. Porcine KLF gene family: Structure, mapping, and phylogenetic analysis. Genomics 2010; 95(2): 111-9. doi: 10.1016/j.ygeno.2009.11.001 PMID: 19941950
  22. Hu F, Ren Y, Wang Z, et al. Bioinformatics analysis of KLF2 as a potential prognostic factor in ccRCC and association with epithelial mesenchymal transition. Exp Ther Med 2022; 24(3): 561. doi: 10.3892/etm.2022.11498 PMID: 35978925
  23. Safi S, Badshah Y, Shabbir M, et al. Predicting 3D structure, cross talks, and prognostic significance of klf9 in cervical cancer. Front Oncol 2022; 11: 797007. doi: 10.3389/fonc.2021.797007 PMID: 35047407
  24. Le NQK, Do DT, Nguyen TTD, Le QA. A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features. Gene 2021; 787: 145643. doi: 10.1016/j.gene.2021.145643 PMID: 33848577
  25. Rose PW, Prlić A, Altunkaya A, et al. The RCSB protein data bank: Integrative view of protein, gene and 3D structural information. Nucleic Acids Res 2017; 45(D1): D271-81. PMID: 27794042
  26. O’Leary NA, Wright MW, Brister JR, et al. Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 2016; 44(D1): D733-45. doi: 10.1093/nar/gkv1189 PMID: 26553804
  27. Bateman A, Martin MJ, Orchard S. UniProt: The universal protein knowledgebase in 2023. Nucleic Acids Res 2022; 49(D1): D480-9.
  28. Li W, Godzik A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 2006; 22(13): 1658-9. doi: 10.1093/bioinformatics/btl158 PMID: 16731699
  29. Xiao N, Cao DS, Zhu MF, Xu QS. protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics 2015; 31(11): 1857-9. doi: 10.1093/bioinformatics/btv042 PMID: 25619996
  30. Chou KC. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 2001; 43(3): 246-55. doi: 10.1002/prot.1035 PMID: 11288174
  31. Chou KC. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 2005; 21(1): 10-9. doi: 10.1093/bioinformatics/bth466 PMID: 15308540
  32. Chen C, Zhang Q, Ma Q, Yu B. LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion. Chemom Intell Lab Syst 2019; 191: 54-64. doi: 10.1016/j.chemolab.2019.06.003
  33. Govindarajan S, Recabarren R, Goldstein RA. Estimating the total number of protein folds. Proteins 1999; 35(4): 408-14. doi: 10.1002/(SICI)1097-0134(19990601)35:43.0.CO;2-A PMID: 10382668
  34. Dubchak I, Muchnik I, Holbrook SR, Kim SH. Prediction of protein folding class using global description of amino acid sequence. Proc Natl Acad Sci 1995; 92(19): 8700-4. doi: 10.1073/pnas.92.19.8700 PMID: 7568000
  35. Malik A, Subramaniyam S, Kim CB, Manavalan B. SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information. Comput Struct Biotechnol J 2022; 20: 165-74. doi: 10.1016/j.csbj.2021.12.014 PMID: 34976319
  36. Malik A, Mahajan N, Dar TA, Kim CB. C10Pred: A first machine learning based tool to predict C10 family cysteine peptidases using sequence-derived features. Int J Mol Sci 2022; 23(17): 9518. doi: 10.3390/ijms23179518 PMID: 36076915
  37. Firoz A, Malik A, Ali HM, Akhter Y, Manavalan B, Kim CB. PRR-HyPred: A two-layer hybrid framework to predict pattern recognition receptors and their families by employing sequence encoded optimal features. Int J Biol Macromol 2023; 234: 123622. doi: 10.1016/j.ijbiomac.2023.123622 PMID: 36773859
  38. Shen J, Zhang J, Luo X, et al. Predicting protein–protein interactions based only on sequences information. Proc Natl Acad Sci 2007; 104(11): 4337-41. doi: 10.1073/pnas.0607879104 PMID: 17360525
  39. Yang N, Pei Y, Wang Y, Zhao L, Zhao P, Li Z. Identifying the antioxidant activity of tripeptides based on sequence information and machine learning. Chemom Intell Lab Syst 2023; 238: 104845. doi: 10.1016/j.chemolab.2023.104845
  40. Chou KC. Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. Biochem Biophys Res Commun 2000; 278(2): 477-83. doi: 10.1006/bbrc.2000.3815 PMID: 11097861
  41. Dong J, Zhu MF, Yun YH, Lu AP, Hou TJ, Cao DS. BioMedR: An R/CRAN package for integrated data analysis pipeline in biomedical study. Brief Bioinform 2021; 22(1): 474-84. doi: 10.1093/bib/bbz150 PMID: 31885044
  42. Akbar S, Rahman AU, Hayat M, Sohail M. cACP: Classifying anticancer peptides using discriminative intelligent model via Chou’s 5-step rules and general pseudo components. Chemom Intell Lab Syst 2020; 196: 103912. doi: 10.1016/j.chemolab.2019.103912
  43. Ong SAK, Lin HH, Chen YZ, Li ZR, Cao Z. Efficacy of different protein descriptors in predicting protein functional families. BMC Bioinformatics 2007; 8(1): 300. doi: 10.1186/1471-2105-8-300 PMID: 17705863
  44. van den Berg BA, Reinders MJT, Roubos JA, Ridder D. SPiCE: A web-based tool for sequence-based protein classification and exploration. BMC Bioinformatics 2014; 15(1): 93. doi: 10.1186/1471-2105-15-93 PMID: 24685258
  45. Kuhn M. Building predictive models in r using the caret package. J Stat Softw 2008; 28(5): 1-26. doi: 10.18637/jss.v028.i05
  46. Ahmad A, Akbar S, Hayat M, Ali F, Khan S, Sohail M. Identification of antioxidant proteins using a discriminative intelligent model of k-space amino acid pairs based descriptors incorporating with ensemble feature selection. Biocybern Biomed Eng 2022; 42(2): 727-35. doi: 10.1016/j.bbe.2020.10.003
  47. Shen H, Chou KC. Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types. Biochem Biophys Res Commun 2005; 334(1): 288-92. doi: 10.1016/j.bbrc.2005.06.087 PMID: 16002049
  48. Akkus A, Güvenir HA. K nearest neighbor classification on feature projections. Proceedings of the Thirteenth International Conference on International Conference on Machine Learning 1996;. 12-9.
  49. Ahmed S, Arif M, Kabir M. PredAoDP: Accurate identification of antioxidant proteins by fusing different descriptors based on evolutionary information with support vector machine. Chemom Intell Lab Syst 2022; 228: 104623.
  50. Rish I. An empirical study of the naive Bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence. 2001; pp. 41-6.
  51. Abbas Z, Tayara H, Chong KT. Alzheimer’s disease prediction based on continuous feature representation using multi-omics data integration. Chemom Intell Lab Syst 2022; 223: 104536. doi: 10.1016/j.chemolab.2022.104536
  52. Breiman L. Random forests. Mach Learn 2001; 45(1): 5-32. doi: 10.1023/A:1010933404324
  53. Jo T, Cheng J. Improving protein fold recognition by random forest. BMC Bioinformatics 2014; 15(S11) (Suppl. 11): S14. doi: 10.1186/1471-2105-15-S11-S14 PMID: 25350499
  54. Li J, Wu J, Chen K. PFP-RFSM: Protein fold prediction by using random forests and sequence motifs. J Biomed Sci Eng 2013; 6(12): 1161-70. doi: 10.4236/jbise.2013.612145
  55. Waris M, Ahmad K, Kabir M, Hayat M. Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix. Neurocomputing 2016; 199: 154-62. doi: 10.1016/j.neucom.2016.03.025
  56. Ma X, Guo J, Sun X. DNABP: Identification of DNA-Binding proteins based on feature selection using a random forest and predicting binding residues. PLoS One 2016; 11(12): e0167345. doi: 10.1371/journal.pone.0167345 PMID: 27907159
  57. Hayat M, Khan A, Yeasin M. Prediction of membrane proteins using split amino acid and ensemble classification. Amino Acids 2012; 42(6): 2447-60. doi: 10.1007/s00726-011-1053-5 PMID: 21850437
  58. Sabooh MF, Iqbal N, Khan M, Khan M, Maqbool HF. Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC. J Theor Biol 2018; 452: 1-9. doi: 10.1016/j.jtbi.2018.04.037 PMID: 29727634
  59. Akbar S, Hayat M, Tahir M. cACP-2LFS: Classification of anticancer peptides using sequential discriminative model of KSAAP and two-level feature selection approach, IEEE Access 8: 131939-48.
  60. Ali F, Arif M, Khan ZU, Kabir M, Ahmed S, Yu DJ. SDBP-Pred: Prediction of single-stranded and double-stranded DNA-binding proteins by extending consensus sequence and K-segmentation strategies into PSSM. Anal Biochem 2020; 589: 113494. doi: 10.1016/j.ab.2019.113494 PMID: 31693872
  61. Akbar S, Hayat M. iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences. J Theor Biol 2018; 455: 205-11. doi: 10.1016/j.jtbi.2018.07.018 PMID: 30031793
  62. Chen T, Guestrin C. Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining 2016;. 785-94. doi: 10.1145/2939672.2939785
  63. Banjar A, Ali F, Alghushairy O. iDBP-PBMD: A machine learning model for detection of DNA-binding proteins by extending compression techniques into evolutionary profile. Chemom Intell Lab Syst 2022; 231: 104697.
  64. Basith S, Lee G, Manavalan B. STALLION: A stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction. Brief Bioinform 2022; 23(1): bbab376. doi: 10.1093/bib/bbab376 PMID: 34532736
  65. Jeon H, Oh S. Hybrid-recursive feature elimination for efficient feature selection. Applied Sciences-Basel 2020; 10: p. (9)3211.
  66. Malik A, Shoombuatong W, Kim CB, Manavalan B. GPApred: The first computational predictor for identifying proteins with LPXTG-like motif using sequence-based optimal features. Int J Biol Macromol 2023; 229: 529-38. doi: 10.1016/j.ijbiomac.2022.12.315 PMID: 36596370
  67. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn 2002; 46(1/3): 389-422. doi: 10.1023/A:1012487302797
  68. Zhang Z, Gong Y, Gao B, et al. SNAREs-SAP: SNARE proteins identification with PSSM profiles. Front Genet 2021; 12: 809001. doi: 10.3389/fgene.2021.809001 PMID: 34987554
  69. Orzechowska-Licari EJ, LaComb JF, Mojumdar A, Bialkowska AB. SP and KLF transcription factors in cancer metabolism. Int J Mol Sci 2022; 23(17): 9956. doi: 10.3390/ijms23179956 PMID: 36077352
  70. Zhong Z, Zhou F, Wang D, et al. Expression of KLF9 in pancreatic cancer and its effects on the invasion, migration, apoptosis, cell cycle distribution, and proliferation of pancreatic cancer cell lines. Oncol Rep 2018; 40(6): 3852-60. doi: 10.3892/or.2018.6760 PMID: 30542730
  71. Liao X, Haldar SM, Lu Y, et al. Krüppel-like factor 4 regulates pressure-induced cardiac hypertrophy. J Mol Cell Cardiol 2010; 49(2): 334-8. doi: 10.1016/j.yjmcc.2010.04.008 PMID: 20433848
  72. Xie W, Li L, Zheng XL, Yin WD, Tang CK. The role of Krüppel-like factor 14 in the pathogenesis of atherosclerosis. Atherosclerosis 2017; 263: 352-60. doi: 10.1016/j.atherosclerosis.2017.06.011 PMID: 28641818
  73. Birsoy K, Chen Z, Friedman J. Transcriptional regulation of adipogenesis by KLF4. Cell Metab 2008; 7(4): 339-47. doi: 10.1016/j.cmet.2008.02.001 PMID: 18396140
  74. Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B. Definitions, methods, and applications in interpretable machine learning. Proc Natl Acad Sci 2019; 116(44): 22071-80. doi: 10.1073/pnas.1900654116 PMID: 31619572
  75. Muggleton S, King RD, Stenberg MJE. Protein secondary structure prediction using logic-based machine learning. Protein Eng Des Sel 1992; 5(7): 647-57. doi: 10.1093/protein/5.7.647 PMID: 1480619
  76. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596(7873): 583-9. doi: 10.1038/s41586-021-03819-2 PMID: 34265844
  77. Malik A, Ahmad S. Sequence and structural features of carbohydrate binding in proteins and assessment of predictability using a neural network. BMC Struct Biol 2007; 7(1): 1. doi: 10.1186/1472-6807-7-1 PMID: 17201922
  78. Firoz A, Malik A, Joplin KH, Ahmad Z, Jha V, Ahmad S. Residue propensities, discrimination and binding site prediction of adenine and guanine phosphates. BMC Biochem 2011; 12(1): 20. doi: 10.1186/1471-2091-12-20 PMID: 21569447
  79. Ahmad S, Sarai A. Moment-based prediction of DNA-binding proteins. J Mol Biol 2004; 341(1): 65-71. doi: 10.1016/j.jmb.2004.05.058 PMID: 15312763
  80. Manavalan B, Patra MC. MLCPP 2.0: An updated cell-penetrating peptides and their uptake efficiency predictor. J Mol Biol 2022; 434(11): 167604. doi: 10.1016/j.jmb.2022.167604 PMID: 35662468
  81. Kurata H, Tsukiyama S, Manavalan B. iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model. Brief Bioinform 2022; 23(4): bbac265. doi: 10.1093/bib/bbac265 PMID: 35772910
  82. Wang YH, Zhang YF, Zhang Y, et al. Identification of adaptor proteins using the ANOVA feature selection technique. Methods 2022; 208: 42-7. doi: 10.1016/j.ymeth.2022.10.008 PMID: 36341922
  83. Dao FY, Liu ML, Su W, et al. AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins. Int J Biol Macromol 2023; 228: 706-14. doi: 10.1016/j.ijbiomac.2022.12.250 PMID: 36584777
  84. Manavalan B, Shin TH, Kim MO, Lee G. PIP-EL: A new ensemble learning method for improved proinflammatory peptide predictions. Front Immunol 2018; 9: 1783. doi: 10.3389/fimmu.2018.01783 PMID: 30108593
  85. Manavalan B, Govindaraj RG, Shin TH, Kim MO, Lee G. iBCE-EL: A new ensemble learning framework for improved linear B-Cell epitope prediction. Front Immunol 2018; 9: 1695. doi: 10.3389/fimmu.2018.01695 PMID: 30100904
  86. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res 2002; 16: 321-57. doi: 10.1613/jair.953
  87. Manavalan B, Shin TH, Lee G. PVP-SVM: Sequence-based prediction of phage virion proteins using a support vector machine. Front Microbiol 2018; 9: 476. doi: 10.3389/fmicb.2018.00476 PMID: 29616000
  88. Qiu WR, Xu A, Xu ZC, Zhang CH, Xiao X. Identifying acetylation protein by fusing its PseAAC and functional domain annotation. Front Bioeng Biotechnol 2019; 7: 311. doi: 10.3389/fbioe.2019.00311 PMID: 31867311
  89. Qiu WR, Xiao X, Xu ZC, Chou KC. iPhos-PseEn: Identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget 2016; 7(32): 51270-83. doi: 10.18632/oncotarget.9987 PMID: 27323404

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