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Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization

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Abstract

In the present paper, deep convolutional neural network (DCNN) is applied to multilocus protein subcellular localization as it is more suitable for multi-class classification. There are two main problems with this application. First, the appropriate features for correlation between multiple sites are hard to find. Second, the classifier structure is difficult to determine as it is greatly affected by the distribution of classified data. To solve these problems, a self-evoluting framework using DCNNs for multilocus protein subcellular localization is proposed. It has three characteristics that the previous algorithms do not. The first is that it combines the ant colony algorithm with the DCNN to form a self-evoluting algorithm for multilocus protein subcellular localization. The second is that it randomly groups subcellular sites using a limited random k-labelsets multi-label classification method. It also solves complex problems in a divide-and-conquer approach and proposes a flexible expansion model. The third is that it realizes the random selection feature extraction method in the positioning process and avoids the defects in individual feature extraction methods. The algorithm in the present paper is tested on the human database, and the overall correct rate is 67.17%, which is higher than that for the stacked self-encoder (SAE), support vector machine (SVM), random forest classifier (RF), or single deep convolutional neural network.

Graphical abstract

The algorithm mentioned in the present paper mainly includes four parts. They are protein sequence data preprocessing, integrated DCNN model construction, finding optimal DCNN combination by ant colony optimization, and protein subcellular localization for sequences. These parts are sequential relationships and the data obtained in the previous part is the basis for the latter part of the function. In the part of data preprocessing, the limited RAkEL multi-label classification method is used to randomly group subcellular sites. At the same time, the feature fusion of protein sequences is carried out by using multiple feature extraction methods. Each combination including features and sites information corresponds to a DCNN model. In the part of finding optimal DCNN combination by ant colony optimization, the main purpose is to find the best combination of DCNN models through the global optimization ability of the ant colony algorithm. The positioning of sequences is mainly to obtain multilocus subcellular localization by the optimal model combination.

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References

  1. Yang L, Lv Y, Li T, Zuo Y, Jiang W (2014) Human proteins characterization with subcellular localizations. J Theor Biol 358:61–73

    CAS  PubMed  Google Scholar 

  2. Ludwik KA, von Kuegelgen N, Chekulaeva M (2019) Genome-wide analysis of RNA and protein localization and local translation in mesc-derived neurons. Methods 162-163:31–41

    CAS  PubMed  Google Scholar 

  3. Wei L, Liao M, Gao X, Wang J, Lin W (2016) mgof-loc: a novel ensemble learning method for human protein subcellular localization prediction. Neurocomputing 217:73–82

    Google Scholar 

  4. Mooney C, Wang Y-H, Pollastri G (2011) Sclpred: protein subcellular localization prediction by n-to-1 neural networks. Bioinformatics 27(20):2812–2819

    CAS  PubMed  Google Scholar 

  5. Zhou H, Yang Y, Shen H-B (2016) Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features. Bioinformatics 33(6):843–853

    Google Scholar 

  6. Wan S, Mak M-W, Kung S-Y (2016) Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins. BMC Bioinf 17(1):97

    Google Scholar 

  7. Mak M-W, Guo J, Kung S-Y (2008) Pairprosvm: protein subcellular localization based on local pairwise profile alignment and svm. IEEE/ACM Trans Comput Biol Bioinform 5(3):416–422

    CAS  PubMed  Google Scholar 

  8. Zhang S, Duan X (2018) Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC. J Theor Biol 437:239–250

    CAS  PubMed  Google Scholar 

  9. Zhao D, Liu H, Zheng Y, He Y, Lu D, Lyu C (2019) A reliable method for colorectal cancer prediction based on feature selection and support vector machine. Med Biol Eng Comput 57(4):901–912

    PubMed  Google Scholar 

  10. Cheng X, Xiao X, Chou K-C (2017) pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics 34(9):1448–1456

    Google Scholar 

  11. Zhang D, Huang H, Bai X, Fang X, Zhang Y (2019) A highprecision hybrid algorithm for predicting eukaryotic protein subcellular localization. BioRxiv, page 620179

  12. Liu Z, Jianjun H (2016) Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction. Methods 93:119–127

    CAS  PubMed  Google Scholar 

  13. Chou K-C (2013) Some remarks on predicting multi-label attributes in molecular biosystems. Mol BioSyst 9(6):1092–1100

    CAS  PubMed  Google Scholar 

  14. Barracchia EP, Pio G, D'Elia D, Ceci M (2020) Prediction of new associations between ncRNAs and diseases exploiting multi-type hierarchical clustering. BMC Bioinf 21(1)

  15. Jiang X, Zhao J, Qian W, Song W, Lin GN (2020) A generative adversarial network model for disease gene prediction with RNA-seq data. IEEE Access 8:37352–37360

    Google Scholar 

  16. Pio G, Ceci M, Prisciandaro F, Malerba D (2019) Exploiting causality in gene network reconstruction based on graph embedding. Mach Learn 109:1231–1279

    Google Scholar 

  17. Li Z, Zhu J, Xu X, Yao Y (2020) Rdense: a protein–RNA binding prediction model based on bidirectional recurrent neural network and densely connected convolutional networks. IEEE Access 8:14588–14605

    Google Scholar 

  18. Mignone P, Pio G, D'Elia D, Ceci M (2019) Exploiting transfer learning for the reconstruction of the human gene regulatory network. Bioinformatics 36(5):1553–1561

    Google Scholar 

  19. Li J, Li Z, Luo J, Yao Y (2020) ACNNT3: attention-CNN framework for prediction of sequence-based bacterial type III secreted effectors. Comput Math Methods Med 2020:1–7

    Google Scholar 

  20. Yang F, Xu Y-Y, Wang S-T, Shen H-B (2014) Image-based classification of protein subcellular location patterns in human reproductive tissue by ensemble learning global and local features. Neurocomputing 131:113–123

    Google Scholar 

  21. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    CAS  Google Scholar 

  22. Wang K, Li K, Zhou L, Hu Y, Cheng Z, Liu J, Chen C (2019) Multiple convolutional neural networks for multivariate time series prediction. Neurocomputing 360:107–119

    Google Scholar 

  23. Alik N, Kurban OC, Yilmaz AR et al (2019) Large-scale offline signature recognition via deep neural networks and feature embedding [J]. Neurocomputing 359

  24. Kleinkauf R, Houwaart T, Backofen R, Mann M (2015) AntaRNA-multiobjective inverse folding of pseudoknot RNA using ant-colony optimization. BMC Bioinf 16(1):389

    Google Scholar 

  25. Wu Y, Gong M, Ma W, Wang S (2019) High-order graph matching based on ant colony optimization. Neurocomputing 328:97–104

    Google Scholar 

  26. Nápoles G, Falcon R, Dikopoulou Z, Papageorgiou E, Bello R, Vanhoof K (2017) Weighted aggregation of partial rankings using ant colony optimization. Neurocomputing 250:109–120

    Google Scholar 

  27. Cheng X, Xiao X, Wu Z-c, Wang P, Lin W-z (2013) Swfoldrate: predicting protein folding rates from amino acid sequence with sliding window method. Proteins: Struct, Funct, Bioinf 81(1):140–148

    CAS  Google Scholar 

  28. Shen H-B, Chou K-C (2010) Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. J Biomol Struct Dyn 28(2):175–186

    CAS  PubMed  Google Scholar 

  29. Mei S (2012) Multi-kernel transfer learning based on chou’s PseAAC formulation for protein submitochondria localization. J Theor Biol 293:121–130

    CAS  PubMed  Google Scholar 

  30. Schmidhuber J (2015) Deep learning in neural networks: an overview [J]. Neural Netw 61:85–117

    PubMed  Google Scholar 

  31. Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352–2449

    PubMed  Google Scholar 

  32. Sui X, Zheng Y, Wei B, Bi H, Wu J, Pan X, Yin Y, Zhang S (2017) Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks. Neurocomputing 237:332–341

    Google Scholar 

  33. Lee C-Y, Gallagher PW, Tu Z (2016) Generalizing pooling functions in convolutional neural networks: mixed, gated, and tree. In: Artificial Intelligence and Statistics, pp 464–472

    Google Scholar 

  34. Wen M, Zhang Z, Niu S, Sha H, Yang R, Yun Y, Lu H (2017) Deep-learning-based drug–target interaction prediction. J Proteome Res 16(4):1401–1409

    CAS  PubMed  Google Scholar 

  35. Reed M, Yiannakou A, Evering R (2014) An ant colony algorithm for the multi-compartment vehicle routing problem. Appl Soft Comput 15:169–176

    Google Scholar 

  36. Gao Y, Guan H, Qi Z, Yang H, Liang L (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79(8):1230–1242

    Google Scholar 

  37. Rogai F, Manfredi C, Bocchi L (2016) Metaheuristics for specialization of a segmentation algorithm for ultrasound images. IEEE Trans Evol Comput 20(5):730–741

    Google Scholar 

  38. Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A (2007) Uniprotkb/swiss-prot. In: Plant bioinformatics. Springer, Berlin, pp 89–112

    Google Scholar 

  39. Jaramillo-Garzon J, Castellanos-Dominguez A et al (2015) Feature extraction by statistical contact potentials and wavelet transform for predicting subcellular localizations in gram negative bacterial proteins [J]. J Theor Biol 364:121–130

    PubMed  Google Scholar 

  40. Lian J, Shi Y, Zhang Y, Jia W, Fan X, Zheng Y (2020) Revealing false positive features in epileptic EEG identification. Int J Neural Syst:2050017–2050017

  41. Mandal M, Mukhopadhyay A, Maulik U (2015) Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou’s PseAAC. Med Biol Eng Comput 53(4):331–344

    PubMed  Google Scholar 

  42. Dorigo M, Birattari M (2010) Ant colony optimization. Springer, Berlin

    Google Scholar 

  43. Dorigo M, Stützle T (2006) The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Handbook of Metaheuristics

    Google Scholar 

  44. LeyiWei YD, Su R, Tang J, Zou Q (2017) Prediction of human protein subcellular localization using deep learning. J Parallel Distrib Comput 117:212–217

    Google Scholar 

  45. Pärnamaa T, Parts L (2017) Accurate classification of protein subcellular localization from high-throughput microscopy images using deep learning. G3: Genes, Genomes, Genetics 7(5):1385–1392

    Google Scholar 

Download references

Acknowledgments

This research was supported by the National Natural Science Foundation of China (61876102, 61472232) and the National Key Research and Development Program of China (No. 2016YFC0106000).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, No. 88, Wenhua East Road, Jinan City, China

    Hanhan Cong & Hong Liu

  2. Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Shandong Normal University, Jinan, China

    Hanhan Cong & Hong Liu

  3. School of Information Science and Engineering, University of Jinan, Jinan, China

    Yuehui Chen & Yi Cao

  4. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China

    Yuehui Chen & Yi Cao

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  1. Hanhan Cong
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  2. Hong Liu
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Correspondence to Hong Liu.

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Cong, H., Liu, H., Chen, Y. et al. Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization. Med Biol Eng Comput 58, 3017–3038 (2020). https://doi.org/10.1007/s11517-020-02275-w

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