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Regular Paper

DeepHBSP: A Deep Learning Framework for Predicting Human Blood-Secretory Proteins Using Transfer Learning

Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
Department of Computer Science, College of Engineering, Shantou University, Shantou 515063, China
Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai 519041, China

both of them guided the completion of this article

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Abstract

The identification of blood-secretory proteins and the detection of protein biomarkers in the blood have an important clinical application value. Existing methods for predicting blood-secretory proteins are mainly based on traditional machine learning algorithms, and heavily rely on annotated protein features. Unlike traditional machine learning algorithms, deep learning algorithms can automatically learn better feature representations from raw data, and are expected to be more promising to predict blood-secretory proteins. We present a novel deep learning model (DeepHBSP) combined with transfer learning by integrating a binary classification network and a ranking network to identify blood-secretory proteins from the amino acid sequence information alone. The loss function of DeepHBSP in the training step is designed to apply descriptive loss and compactness loss to the binary classification network and the ranking network, respectively. The feature extraction subnetwork of DeepHBSP is composed of a multi-lane capsule network. Additionally, transfer learning is used to train a highly accurate generalized model with small samples of blood-secretory proteins. The main contributions of this study are as follows: 1) a novel deep learning architecture by integrating a binary classification network and a ranking network is proposed, superior to existing traditional machine learning algorithms and other state-of-the-art deep learning architectures for biological sequence analysis; 2) the proposed model for blood-secretory protein prediction uses only amino acid sequences, overcoming the heavy dependence of existing methods on annotated protein features; 3) the blood-secretory proteins predicted by our model are statistically significant compared with existing blood-based biomarkers of cancer.

Keywords

blood-secretory proteindeep learningcapsule networktransfer learning

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References

[1]

Nagpal M, Singh S, Singh P, Chauhan P, Zaidi M A. Tumor markers: A diagnostic tool. National Journal of Maxillofacial Surgery, 2016, 7(1): 17-20. https://doi.org/10.4103/0975-5950.196135.
Crossref Google Scholar
[2]

Loke S Y, Lee A S G. The future of blood-based biomarkers for the early detection of breast cancer. European Journal of Cancer, 2018, 92: 54-68. https://doi.org/10.1016/j.ejca.2017.12.025.
Crossref Google Scholar
[3]

Geyer P E, Kulak N A, Pichler G, Holdt L M, Teupser D, Mann M. Plasma proteome profiling to assess human health and disease. Cell Systems, 2016, 2(3): 185-195. https://doi.org/10.1016/j.cels.2016.02.015.
Crossref Google Scholar
[4]

Cui J, Liu Q, Puett D, Xu Y. Computational prediction of human proteins that can be secreted into the bloodstream. Bioinformatics, 2008, 24(20): 2370-2375. https://doi.org/10.1093/bioinformatics/btn418.
Crossref Google Scholar
[5]

Dhanasekaran S M, Barrette T R, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta K J, Rubin M A, Chinnaiyan A M. Delineation of prognostic biomarkers in prostate cancer. Nature, 2001, 412(6849): 822-826. https://doi.org/10.1038/35090585.
Crossref Google Scholar
[6]

Liu Q, Cui J, Yang Q, Xu Y. In-silico prediction of blood-secretory human proteins using a ranking algorithm. BMC Bioinformatics, 2010, 11: Article No. 250. https://doi.org/10.1186/1471-2105-11-250.
Crossref Google Scholar
[7]

Robinson J L, Feizi A, Uhlén M, Nielsen J. A systematic investigation of the malignant functions and diagnostic potential of the cancer secretome. Cell Reports, 2019, 26(10): 2622-2635. https://doi.org/10.1016/j.celrep.2019.02.025.
Crossref Google Scholar
[8]

Geyer P E, Holdt L M, Teupser D, Mann M. Revisiting biomarker discovery by plasma proteomics. Molecular Systems Biology, 2017, 13(9): Article No. 942. https://doi.org/10.15252/msb.20156297.
Crossref Google Scholar
[9]

Huang L, Shao D, Wang Y, Cui X, Li Y, Chen Q, Cui J. Human body-fluid proteome: Quantitative profiling and computational prediction. Briefings in Bioinformatics, 2021, 22(1): 315-333. https://doi.org/10.1093/bib/bbz160.
Crossref Google Scholar
[10]

Zhang J, Chai H, Guo S, Guo H, Li Y. High-throughput identification of mammalian secreted proteins using species-specific scheme and application to human proteome. Molecules, 2018, 23(6): Article No. 1448. https://doi.org/10.3390/molecules23061448.
Crossref Google Scholar
[11]

Zhang J, Zhang Y, Ma Z. In silico prediction of human secretory proteins in plasma based on discrete firefly optimization and application to cancer biomarkers identification. Frontiers in Genetics, 2019, 10: Article No. 542. https://doi.org/10.3389/fgene.2019.00542.
Crossref Google Scholar
[12]

Wang D, Zeng S, Xu C, Qiu W, Liang Y, Joshi T, Xu D. MusiteDeep: A deep-learning framework for general and kinase-specific phosphorylation site prediction. Bioinformatics, 2017, 33(24): 3909-3916. https://doi.org/10.1093/bioinformatics/btx496.
Crossref Google Scholar
[13]

Liang H, Sun X, Sun Y, Gao Y. Text feature extraction based on deep learning: A review. EURASIP Journal on Wireless Communications and Networking, 2017, 2017: Article No. 211. https://doi.org/10.1186/s13638-017-0993-1.
Crossref Google Scholar
[14]

Cao Z, Du W, Li G, Cao H. DEEPSMP: A deep learning model for predicting the ectodomain shedding events of membrane proteins. Journal of Bioinformatics Computational Biology, 2020, 18(3): Article No. 2050017. https://doi.org/10.1142/S0219720020500171.
Crossref Google Scholar
[15]

Du W, Pang R, Li G, Cao H, Li Y, Liang Y. DeepUEP: Prediction of urine excretory proteins using deep learning. IEEE Access, 2020, 8: 100251-100261. https://doi.org/10.1109/ACCESS.2020.2997937.
Crossref Google Scholar
[16]

Altschul S F, Madden T L, Schäffer A A, Zhang J, Zhang Z, Miller W, Lipman D J. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Research, 1997, 25(17): 3389-3402. https://doi.org/10.1093/nar/25.17.3389.
Crossref Google Scholar
[17]

The UniProt Consortium. UniProt: The universal protein knowledgebase. Nucleic Acids Research, 2017, 45(D1): D158-D169. https://doi.org/10.1093/nar/gkw1099.
Crossref Google Scholar
[18]

Meinken J, Walker G, Cooper C R, Min X J. MetazSecKB: The human and animal secretome and subcellular proteome knowledgebase. Database, 2015: Article No. bav077. https://doi.org/10.1093/database/bav077.
Crossref Google Scholar
[19]

Omenn G S. The HUPO human plasma proteome project. Proteomics Clinical Applications, 2007, 1(8): 769-779. https://doi.org/10.1002/prca.200700369.
Crossref Google Scholar
[20]

Li S J, Peng M, Li H, Liu B S, Wang C, Wu J R, Li Y X, Zeng R. Sys-BodyFluid: A systematical database for human body uid proteome research. Nucleic Acids Research, 2009, 37(Database Issue): D907-D912. https://doi.org/10.1093/nar/gkn849.
Crossref Google Scholar
[21]

Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT suite: A web server for clustering and comparing biological sequences. Bioinformatics, 2010, 26(5): 680-682. https://doi.org/10.1093/bioinformatics/btq003.
Crossref Google Scholar
[22]

Maurer-Stroh S, Debulpaep M, Kuemmerer N et al. Exploring the sequence determinants of amyloid structure using position-specific scoring matrices. Nature Methods, 2010, 7(3): 237-242. https://doi.org/10.1038/nmeth.1432.
Crossref Google Scholar
[23]

Suzek B E, Wang Y, Huang H, McGarvey P B, Wu C H, the UniProt Consortium. UniRef clusters: A comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics, 2015, 31(6): 926-932. https://doi.org/10.1093/bioinformatics/btu739.
Crossref Google Scholar
[24]

Magnan C N, Baldi P. SSpro/ACCpro 5: Almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics, 2014, 30(18): 2592-2597. https://doi.org/10.1093/bioinformatics/btu352.
Crossref Google Scholar
[25]

Perera P, Patel V M. Learning deep features for one-class classification. IEEE Transactions on Image Processing, 2019, 28(11): 5450-5463. https://doi.org/10.1109/TIP.2019.2917862.
Crossref Google Scholar
[26]
Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules. In Proc. the 31st International Conference on Neural Information Processing Systems, Dec. 2017, pp.3856-3866. https://doi.org/10.5555/3294996.3295142.
[27]
Li Y, Yuan Y. Convergence analysis of two-layer neural networks with ReLU activation. In Proc. the 31st International Conference on Neural Information Processing Systems, Dec. 2017, pp.597-607. https://doi.org/10.5555/3294771.3294828.
[28]

Armenteros J J A, Sønderby C K, Sønderby S K, Nielsen H, Winther O. DeepLoc: Prediction of protein subcellular localization using deep learning. Bioinformatics, 2017, 33(21): 3387-3395. https://doi.org/10.1093/bioinformatics/btx431.
Crossref Google Scholar
[29]

Wang D, Liang Y, Xu D. Capsule network for protein post-translational modification site prediction. Bioinformatics, 2019, 35(14): 2386-2394. https://doi.org/10.1093/bioinformatics/bty977.
Crossref Google Scholar
[30]
Caruana R. Learning many related tasks at the same time with backpropagation. In Proc. the 1994 International Conference on Neural Information Processing Systems, Jan. 1994, pp.657-664. https://doi.org/10.5555/2998687.2998769.
[31]
Ng H W, Nguyen V D, Vonikakis V, Winkler S. Deep learning for emotion recognition on small datasets using transfer learning. In Proc. the 2015 ACM International Conference Multimodal Interaction, Nov. 2015, pp.443-449. https://doi.org/10.1145/2818346.2830593.
Crossref
[32]

Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
Google Scholar
[33]

Yao Y, Rosasco L, Caponnetto A. On early stopping in gradient descent learning. Constructive Approximatio, 2007, 26(2): 289-315. https://doi.org/10.1007/s00365-006-0663-2.
Crossref Google Scholar
[34]

Jurtz V I, Johansen A R, Nielsen M, Armenteros J J A, Nielsen H, Sønderby C K, Winther O, Sønderby S K. An introduction to deep learning on biological sequence data: Examples and solutions. Bioinformatics, 2017, 33(22): 3685-3690. https://doi.org/10.1093/bioinformatics/btx531.
Crossref Google Scholar
[35]
Kingma D P, Ba J. Adam: A method for stochastic optimization. arXiv: 1412.6980, 2014. http://arxiv.org/abs/14-12.6980, May 2020.
[36]

Matthews B W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) — Protein Structure, 1975, 405(2): 442-451. https://doi.org/10.1016/0005-2795(75)90109-9.
Crossref Google Scholar
[37]

Linden A. Measuring diagnostic and predictive accuracy in disease management: An introduction to receiver operating characteristic (ROC) analysis. Journal of Evaluation in Clinical Practice, 2006, 12(2): 132-139. https://doi.org/10.1111/j.1365-2753.2005.00598.x.
Crossref Google Scholar
[38]

Savojardo C, Martelli P L, Fariselli P, Casadio R. Deep-Sig: Deep learning improves signal peptide detection in proteins. Bioinformatics, 2018, 34(10): 1690-1696. https://doi.org/10.1093/bioinformatics/btx818.
Crossref Google Scholar
[39]

Quang D, Xie X. DanQ: A hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Research, 2016, 44(11): Article No. e107. https://doi.org/10.1093/nar/gkw226.
Crossref Google Scholar
[40]

Du W, Sun Y, Li G, Cao H, Pang R, Li Y. CapsNet-SSP: Multilane capsule network for predicting human saliva-secretory proteins. BMC Bioinformatics, 2020, 21(1): Article No. 237. https://doi.org/10.1186/s12859-020-03579-2.
Crossref Google Scholar
[41]

Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi A H, Tanaseichuk O, Benner C, Chanda S K. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications, 2019, 10(1): Article No. 1523. https://doi.org/10.1038/s41467-019-09234-6.
Crossref Google Scholar
[42]

Emilsson V, Ilkov M, Lamb J R et al. Co-regulatory networks of human serum proteins link genetics to disease. Science, 2018, 361(6404): 769-773. https://doi.org/10.1126/science.aaq1327.
Crossref Google Scholar
[43]

Ahn S B, Sharma S, Mohamedali A et al. Potential early clinical stage colorectal cancer diagnosis using a proteomics blood test panel. Clinical Proteomics, 2019, 16: Article No. 34. https://doi.org/10.1186/s12014-019-9255-z.
Crossref Google Scholar
[44]

Ahn J M, Sung H J, Yoon Y H, Kim B G, Yang W S, Lee C, Park H M, Kim B J, Kim B G, Lee S Y, An H J, Cho J Y. Integrated glycoproteomics demonstrates fucosylated serum paraoxonase 1 alterations in small cell lung cancer. Molecular & Cellular Proteomics, 2014, 13(1): 30-48. https://doi.org/10.1074/mcp.M113.028621.
Crossref Google Scholar
Journal of Computer Science and Technology
Volume 36 Issue 2,
March 2021
Pages 234-247
DOI: 10.1007/s11390-021-0851-9
Cite this article:
Du W, Sun Y, Bao H-M, et al. DeepHBSP: A Deep Learning Framework for Predicting Human Blood-Secretory Proteins Using Transfer Learning. Journal of Computer Science and Technology, 2021, 36(2): 234-247. https://doi.org/10.1007/s11390-021-0851-9

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Received: 30 July 2020
Accepted: 28 February 2021
Published: 05 March 2021
©Institute of Computing Technology, Chinese Academy of Sciences 2021
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