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Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (2): 234-247.doi: 10.1007/s11390-021-0851-9
Special Issue: Emerging Areas
• Special Section on AI and Big Data Analytics in Biology and Medicine • Previous Articles Next Articles
Wei Du1, Member, CCF, IEEE, Yu Sun1, Hui-Min Bao1, Liang Chen2, Member, CCF, Ying Li1,*, Senior Member, CCF, and Yan-Chun Liang1,3,*, Senior Member, CCF
[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. DOI:10.4103/0975-5950.196135. [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. DOI:10.1016/j.ejca.2017.12.025. [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. DOI:10.1016/j.cels.2016.02.015. [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. DOI:10.1093/bioinformatics/btn418. [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. DOI:10.1038/35090585. [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. DOI:10.1186/1471-2105-11-250. [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. DOI:10.1016/j.celrep.2019.02.025. [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. DOI:10.15252/msb.20156297. [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. DOI:10.1093/bib/bbz160. [10] Zhang J, Chai H, Guo S, Guo H, Li Y. Highthroughput identification of mammalian secreted proteins using species-specific scheme and application to human proteome. Molecules, 2018, 23(6):Article No. 1448. DOI:10.3390/molecules23061448. [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. DOI:10.3389/fgene.2019.00542. [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. DOI:10.1093/bioinformatics/btx496. [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. DOI:10.1186/s13638-017-0993-1. [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. DOI:10.1142/S0219720020500171. [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. DOI:10.1109/ACCESS.2020.2997937. [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. DOI:10.1093/nar/25.17.3389. [17] The UniProt Consortium. UniProt:The universal protein knowledgebase. Nucleic Acids Research, 2017, 45(D1):D158-D169. DOI:10.1093/nar/gkw1099. [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. DOI:10.1093/database/bav077. [19] Omenn G S. The HUPO human plasma proteome project. Proteomics Clinical Applications, 2007, 1(8):769-779. DOI:10.1002/prca.200700369. [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 fluid proteome research. Nucleic Acids Research, 2009, 37(Database Issue):D907-D912. DOI:10.1093/nar/gkn849. [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. DOI:10.1093/bioinformatics/btq003. [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. DOI:10.1038/nmeth.1432. [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. DOI:10.1093/bioinformatics/btu739. [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. DOI:10.1093/bioinformatics/btu352. [25] Perera P, Patel V M. Learning deep features for one-class classification. IEEE Transactions on Image Processing, 2019, 28(11):5450-5463. DOI:10.1109/TIP.2019.2917862. [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. DOI: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. DOI: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. DOI:10.1093/bioinformatics/btx431. [29] Wang D, Liang Y, Xu D. Capsule network for protein post-translational modification site prediction. Bioinformatics, 2019, 35(14):2386-2394. DOI:10.1093/bioinformatics/bty977. [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. DOI: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. DOI:10.1145/2818346.2830593. [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. [33] Yao Y, Rosasco L, Caponnetto A. On early stopping in gradient descent learning. Constructive Approximatio, 2007, 26(2):289-315. DOI:10.1007/s00365-006-0663-2. [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. DOI:10.1093/bioinformatics/btx531. [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. DOI:10.1016/0005-2795(75)90109-9. [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. DOI:10.1111/j.1365-2753.2005.00598.x. [38] Savojardo C, Martelli P L, Fariselli P, Casadio R. DeepSig:Deep learning improves signal peptide detection in proteins. Bioinformatics, 2018, 34(10):1690-1696. DOI:10.1093/bioinformatics/btx818. [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. DOI:10.1093/nar/gkw226. [40] Du W, Sun Y, Li G, Cao H, Pang R, Li Y. CapsNet-SSP:Multilane capsule network for predicting human salivasecretory proteins. BMC Bioinformatics, 2020, 21(1):Article No. 237. DOI:10.1186/s12859-020-03579-2. [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 systemslevel datasets. Nature Communications, 2019, 10(1):Article No. 1523. DOI:10.1038/s41467-019-09234-6. [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. DOI:10.1126/science.aaq1327. [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. DOI:10.1186/s12014-019-9255-z. [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. DOI:10.1074/mcp.M113.028621. |
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