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Research Paper

Identification of intelligence-related proteins through a robust two-layer predictor

, , ORCID Icon, , & ORCID Icon
Pages 253-264 | Received 13 Oct 2022, Accepted 31 Oct 2022, Published online: 15 Nov 2022

References

  • Legg S, Hutter M. A collection of definitions of intelligence. Front Artif Intell Appl. 2007a;157:17.
  • Legg S, Hutter M. Universal intelligence: a definition of machine intelligence. Minds Machines. 2007b;17:391–444.
  • Trewavas A. Green plants as intelligent organisms. Trends Plant Sci. 2005;10:413–419.
  • Witzany G. Memory and Learning as Key Competences of Living Organisms. Memory and Learning in Plants. Springer; 2018. p. 1–16.
  • Byrne JH, Hawkins RD. Nonassociative learning in invertebrates. Cold Spring Harb Perspect Biol. 2015;7:a021675.
  • de Vargas LDS, Sevenster D, Lima KR, et al. Novelty exposure hinders aversive memory generalization and depends on hippocampal protein synthesis. Behav Brain Res. 2019;359:89–94.
  • Lamprecht R, Farb CR, LeDoux JE. Fear memory formation involves p190 RhoGAP and ROCK proteins through a GRB2-mediated complex. Neuron. 2002;36:727–738.
  • Zhang YY, Liu MY, Liu Z, et al. GPR30‐mediated estrogenic regulation of actin polymerization and spatial memory involves SRC‐1 and PI3K‐mTORC2 in the hippocampus of female mice. CNS Neurosci Ther. 2019;25:714–733.
  • Day JJ, Sweatt JD. Cognitive neuroepigenetics: a role for epigenetic mechanisms in learning and memory. Neurobiol Learn Mem. 2011;96:2–12.
  • Calvo P, Baluška F, Trewavas A. Integrated information as a possible basis for plant consciousness. Biochem Biophys Res Commun. 2020.
  • Bakhtiarizadeh MR, Moradi-Shahrbabak M, Ebrahimi M, et al. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology. J Theor Biol. 2014;356:213–222.
  • Oldfield CJ, Uversky VN, Kurgan L. Predicting functions of disordered proteins with MoRFpred Computational methods in protein evolution. Springer; 2019. p. 337–352.
  • Qin Y, Zheng X, Wang J, et al. Prediction of protein structural class based on Linear Predictive Coding of PSI-BLAST profiles. Open Life Sci. 2015;10.
  • Yao Y, Li M, Xu H, et al. Protein Subcellular localization prediction based on PSI-BLAST profile and principal component analysis. Curr Proteomics. 2019;16:402–414.
  • Xiong J. Essential bioinformatics. Cambridge University Press; 2006.
  • Yang Y, Zheng H, Wang C, et al. Predicting apoptosis protein subcellular locations based on the protein overlapping property matrix and tri-gram encoding. Int J Mol Sci. 2019;20:2344.
  • Bakhtiarizadeh MR, Rahimi M, Mohammadi-Sangcheshmeh A, et al. PrESOgenesis: a two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach. Sci Rep. 2018b;8:9025.
  • Garg A, Raghava GP. A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search. In: silico biology. Vol. 8. 2008. p. 129–140.
  • Gromiha MM, Ahmad S, Suwa M, 2008. Neural network based prediction of protein structure and Function: comparison with other machine learning methods. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, pp. 1739–1744.
  • Jamali AA, Ferdousi R, Razzaghi S, et al. DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins. Drug Discov Today. 2016;21:718–724.
  • Rahimi M, Bakhtiarizadeh MR, Mohammadi-Sangcheshmeh A. OOgenesis_Pred: a sequence-based method for predicting oogenesis proteins by six different modes of Chou’s pseudo amino acid composition. J Theor Biol. 2017;414:128–136.
  • Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273–297.
  • Bakhtiarizadeh MR, Rahimi M, Mohammadi-Sangcheshmeh A, et al. PrESOgenesis: a two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach. Sci Rep. 2018a;8:1–12.
  • Cui G, Fang C, Han K. Prediction of protein-protein interactions between viruses and human by an SVM model. In: BMC Bioinformatics. Vol. 13. Springer; 2012. p. S5.
  • Romero‐Molina S, Ruiz‐Blanco YB, Harms M, et al. PPI‐Detect: a support vector machine model for sequence‐based prediction of protein–protein interactions. J Comput Chem. 2019;40:1233–1242.
  • You Z, Ming Z, Niu B, et al., 2013. A SVM-based system for predicting protein-protein interactions using a novel representation of protein sequences. International Conference on Intelligent Computing. Springer, pp. 629–637.
  • Houssein EH, Hosney ME, Oliva D, et al. A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery. Comput Chem Eng. 2020;133:106656.
  • Gupta S, Kapoor P, Chaudhary K, et al. In silico approach for predicting toxicity of peptides and proteins. PloS one. 2013;8:e73957.
  • Islam SA, Sajed T, Kearney CM, et al. PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides. BMC Bioinformatics. 2015;16:210.
  • Gubbi J, Lai DT, Palaniswami M, et al. Protein secondary structure prediction using support vector machines and a new feature representation. Int J Comput Intell Appl. 2006;6:551–567.
  • Patel M, Shah H, 2013. Protein secondary structure prediction using support vector machines (SVMs). 2013 International Conference on Machine Intelligence and Research Advancement, Katra, India. IEEE, pp. 594–598.
  • Meng C, Jin S, Wang L, et al. AOPs-SVM: a sequence-based classifier of antioxidant proteins using a support vector machine. Front Bioeng Biotechnol. 2019;7.
  • Aruna S, Rajagopalan S. A novel SVM based CSSFFS feature selection algorithm for detecting breast cancer. Int J Comput Appl. 2011;31.
  • Cinelli M, Sun Y, Best K, et al. Feature selection using a one dimensional naïve Bayes’ classifier increases the accuracy of support vector machine classification of CDR3 repertoires. Bioinformatics. 2017;33:951–955.
  • Guo H, Liu B, Cai D, et al. Predicting protein–protein interaction sites using modified support vector machine. Int J Mach Learn Cybern. 2018;9:393–398.
  • Chen W, Ding H, Feng P, et al. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget. 2016;7:16895.
  • Liu B, Liu F, Wang X, et al. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res. 2015a;43:W65–W71.
  • Xiao X, Wang P, Lin W-Z, et al. iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem. 2013;436:168–177.
  • Chou K-C. Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol. 2011;273:236–247.
  • Chou K-C. Proposing 5-steps rule is a notable milestone for studying molecular biology. Nat Sci. 2020;12:74.
  • Cheng X, Xiao X, Chou K-C. pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. Mol Biosyst. 2017;13:1722–1727.
  • Liu B, Yang F, Chou K-C. 2L-piRNA: a two-layer ensemble classifier for identifying piwi-interacting RNAs and their function. Mol Ther Nucleic Acids. 2017;7:267–277.
  • Eitrich T, Lang B. Efficient optimization of support vector machine learning parameters for unbalanced datasets. J Comput Appl Math. 2006;196:425–436.
  • Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res. 2000;28:235–242.
  • Zhang J, Liu B. A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods. Curr Bioinf. 2019;14:190–199.
  • Ahmad J, Hayat M. MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components. J Theor Biol. 2019;463:99–109.
  • Butt AH, Rasool N, Khan YD. Prediction of antioxidant proteins by incorporating statistical moments based features into Chou’s PseAAC. J Theor Biol. 2019;473:1–8.
  • Chen Z, Zhao P, Li F, et al., 2019. iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Briefings in bioinformatics.
  • Ahmad K, Waris M, Hayat M. Prediction of protein submitochondrial locations by incorporating dipeptide composition into Chou’s general pseudo amino acid composition. J Membr Biol. 2016;249:293–304.
  • Chou K-C, Shen H-B. Recent advances in developing web-servers for predicting protein attributes. Nat Sci. 2009;1:63.
  • Dehzangi A, Heffernan R, Sharma A, et al. Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳ s general PseAAC. J Theor Biol. 2015;364:284–294.
  • Kabir M, Hayat M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol Genet Genomics. 2016;291:285–296.
  • Tang H, Chen W, Lin H. Identification of immunoglobulins using Chou’s pseudo amino acid composition with feature selection technique. Mol Biosyst. 2016;12:1269–1275.
  • Chen W, Lin H, Chou K-C. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol Biosyst. 2015;11:2620–2634.
  • Liu B, Fang L, Wang S, et al. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. J Theor Biol. 2015b;385:153–159.
  • Cai C, Wang W, Sun L, et al. Protein function classification via support vector machine approach. Math Biosci. 2003a;185:111–122.
  • Lin H, Chen W, Ding H. AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes. PloS one. 2013;8:e75726.
  • Mondal S, Pai PP. Chou׳ s pseudo amino acid composition improves sequence-based antifreeze protein prediction. J Theor Biol. 2014;356:30–35.
  • Zhu -P-P, Li W-C, Zhong Z-J, et al. Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition. Mol Biosyst. 2015;11:558–563.
  • Cai Y-D, Zhou G-P, Chou K-C. Support vector machines for predicting membrane protein types by using functional domain composition. Biophys J. 2003c;84:3257–3263.
  • Chou K-C, Cai Y-D. Using functional domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem. 2002;277:45765–45769.
  • Bhasin M, Raghava GP. Classification of nuclear receptors based on amino acid composition and dipeptide composition. J Biol Chem. 2004;279:23262–23266.
  • Chen K, Jiang Y, Du L, et al. Prediction of integral membrane protein type by collocated hydrophobic amino acid pairs. J Comput Chem. 2009;30:163–172.
  • Lee T-Y, Lin Z-Q, Hsieh S-J, et al. Exploiting maximal dependence decomposition to identify conserved motifs from a group of aligned signal sequences. Bioinformatics. 2011;27:1780–1787.
  • 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;34:2499–2502.
  • Feng Z-P, Zhang C-T. Prediction of membrane protein types based on the hydrophobic index of amino acids. J Protein Chem. 2000;19:269–275.
  • Horne DS. Prediction of protein helix content from an autocorrelation analysis of sequence hydrophobicities. Biopolymers. 1988;27:451–477.
  • Sokal RR, Thomson BA. Population structure inferred by local spatial autocorrelation: an example from an Amerindian tribal population. Am J Phys Anthropol. 2006;129:121–131.
  • Cai C, Han L, Ji Z, et al. Enzyme family classification by support vector machines. Proteins Struct Funct Bioinf. 2004;55:66–76.
  • Cai C, Han L, Ji ZL, et al. SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 2003b;31:3692–3697.
  • Dubchak I, Muchnik I, Holbrook SR, et al., 1995. Prediction of protein folding class using global description of amino acid sequence. Proceedings of the National Academy of Sciences 92, 8700–8704.
  • Dubchak I, Muchnik I, Mayor C, et al. Recognition of a protein fold in the context of the SCOP classification. Proteins Struct Funct Bioinf. 1999;35:401–407.
  • Shen J, Zhang J, Luo X, et al., 2007. Predicting protein–protein interactions based only on sequences information. Proceedings of the National Academy of Sciences 104, 4337–4341.
  • Chou K-C. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins: structure. Funct Bioinf. 2001a;43:246–255.
  • Chou K-C. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics. 2004;21:10–19.
  • Chou K-C. Using subsite coupling to predict signal peptides. Protein Eng Des Sel. 2001b;14:75–79.
  • Lin S-W, Lee Z-J, Chen S-C, et al. Parameter determination of support vector machine and feature selection using simulated annealing approach. Appl Soft Comput. 2008;8:1505–1512.
  • Lata S, Sharma B, Raghava G. Analysis and prediction of antibacterial peptides. BMC Bioinformatics. 2007;8:263.
  • Ng XY, Rosdi BA, Shahrudin S. Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity. Biomed Res Int. 2015;2015.
  • Thakur N, Qureshi A, Kumar M. AVPpred: collection and prediction of highly effective antiviral peptides. Nucleic Acids Res. 2012;40:W199–W204.
  • Chen W, Feng P-M, Lin H, et al. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res. 2013;41:e68–e68.
  • Lauria M, Rossi V. Origin of Epigenetic Variation in Plants: relationship with Genetic Variation and Potential Contribution to Plant Memory. Memory and Learning in Plants. Springer; 2018. p. 111–130.
  • Zovkic IB, Guzman-Karlsson MC, Sweatt JD. Epigenetic regulation of memory formation and maintenance. Learn Memory. 2013;20:61–74.
  • Volkov AG, Foster JC, Baker KD, et al. Mechanical and electrical anisotropy in Mimosa pudica pulvini. Plant Signal Behav. 2010;5:1211–1221.
  • Markel K. Lack of evidence for associative learning in pea plants. Elife. 2020a;9:e57614.
  • Markel K. Response to comment on’Lack of evidence for associative learning in pea plants’. ELife. 2020b;9:e61689.
  • Gagliano M, Vyazovskiy VV, Borbély AA, et al. Comment on’Lack of evidence for associative learning in pea plants’. Elife. 2020;9:e61141.
  • Bhandawat A, Jayaswall K, Sharma H, et al. Sound as a stimulus in associative learning for heat stress in Arabidopsis. Commun Integr Biol. 2020;13:1–5.
  • Gagliano M, Vyazovskiy VV, Borbély AA, et al. Learning by association in plants. Sci Rep. 2016;6:38427.
  • Calvo P. The philosophy of plant neurobiology: a manifesto. Synthese. 2016;193:1323–1343.