In silico prediction methods of self-interacting proteins: an empirical and academic survey

doi:10.1007/s11704-022-1563-1

Front. Comput. Sci.

2023, Vol. 17

Issue (3) : 173901 https://doi.org/10.1007/s11704-022-1563-1

REVIEW ARTICLE

In silico prediction methods of self-interacting proteins: an empirical and academic survey

Zhanheng CHEN¹, Zhuhong YOU²(

), Qinhu ZHANG³, Zhenhao GUO³, Siguo WANG³, Yanbin WANG⁴

¹. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
². School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
³. Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
⁴. School of Cyber Science and Technology, Zhejiang University, Hangzhou 310058, China

Download: PDF(5754 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

In silico prediction of self-interacting proteins (SIPs) has become an important part of proteomics. There is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab experiments. The goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction, to provide important references for actual work in the future. In this review, we first describe the data required for the task of DTIs prediction. Then, some interesting feature extraction methods and computational models are presented on this topic in a timely manner. Afterwards, an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding schemes. Overall, we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.

Keywords proteomics self-interacting proteins feature extraction prediction model

Corresponding Author(s): Zhuhong YOU

About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 18 March 2022 Issue Date: 09 October 2022

Cite this article:

Zhanheng CHEN,Zhuhong YOU,Qinhu ZHANG, et al. In silico prediction methods of self-interacting proteins: an empirical and academic survey[J]. Front. Comput. Sci., 2023, 17(3): 173901.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-022-1563-1
https://academic.hep.com.cn/fcs/EN/Y2023/V17/I3/173901

Fig.1 The traditional biological prediction methods

Fig.2 The flowchart of SIPs prediction model

Tab.1 The popular used databases

Tab.2 The short names of 20 amino acids

Fig.3 The Liu’s encoding scheme [38]

Fig.4 Wang’s magnitude of the Zernike moments with low order [30]

Fig.5 Wang’s architecture of SSAE [51]

Fig.6 Wang’s stacked long short-term memory network [30]

Fig.7 The structure of LSTM

Fig.8 Chen’s structure of cascade forest [76]

Fig.9 Chen’s flow chart of multi-grained scanning method [76]

Tab.3 Comparison of various models on human dataset

Tab.4 Comparison of various models on yeast dataset

Tab.5 AUC of various models on yeast dataset

Tab.6 AUC of various models on human dataset

1	J D, Watson R M Cook-Deegan . Origins of the human genome project. The FASEB Journal, 1991, 5( 1): 8– 11
2	S, Min B, Lee S Yoon . Deep learning in bioinformatics. Briefings in Bioinformatics, 2017, 18( 5): 851– 869
3	P, Larrañaga B, Calvo R, Santana C, Bielza J, Galdiano I, Inza J A, Lozano R, Armañanzas G, Santafé A Pérez . Machine learning in bioinformatics. Briefings in Bioinformatics, 2006, 7( 1): 86– 112
4	A D, Baxevanis G, Bader D Wishart. Bioinformatics. John Wiley & Sons, 2020
5	D L Black . Protein diversity from alternative splicing: a challenge for bioinformatics and post-genome biology. Cell, 2000, 103( 3): 367– 370
6	P James . Protein identification in the post-genome era: the rapid rise of proteomics. Quarterly Reviews of Biophysics, 1997, 30( 4): 279– 331
7	D, Eisenberg E M, Marcotte I, Xenarios T O Yeates . Protein function in the post-genomic era. Nature, 2000, 405( 6788): 823– 826
8	M, Kanehisa P Bork . Bioinformatics in the post-sequence era. Nature Genetics, 2003, 33( 3): 305– 310
9	D, Medini D, Serruto J, Parkhill D A, Relman C, Donati R, Moxon S, Falkow R Rappuoli . Microbiology in the post-genomic era. Nature Reviews Microbiology, 2008, 6( 6): 419– 430
10	S Hanash . Disease proteomics. Nature, 2003, 422( 6928): 226– 232
11	J F, Rual K, Venkatesan T, Hao T, Hirozane-Kishikawa A, Dricot N, Li G F, Berriz F D, Gibbons M, Dreze N, Ayivi-Guedehoussou N, Klitgord C, Simon M, Boxem S, Milstein J, Rosenberg D S, Goldberg L V, Zhang S L, Wong G, Franklin S, Li J S, Albala J, Lim C, Fraughton E, Llamosas S, Cevik C, Bex P, Lamesch R S, Sikorski J, Vandenhaute H Y, Zoghbi A, Smolyar S, Bosak R, Sequerra L, Doucette-Stamm M E, Cusick D E, Hill F P, Roth M Vidal . Towards a proteome-scale map of the human protein–protein interaction network. Nature, 2005, 437( 7062): 1173– 1178
12	U, Stelzl U, Worm M, Lalowski C, Haenig F H, Brembeck H, Goehler M, Stroedicke M, Zenkner A, Schoenherr S, Koeppen J, Timm S, Mintzlaff C, Abraham N, Bock S, Kietzmann A, Goedde E, Toksöz A, Droege S, Krobitsch B, Korn W, Birchmeier H, Lehrach E E Wanker . A human protein-protein interaction network: a resource for annotating the proteome. Cell, 2005, 122( 6): 957– 968
13	B, Blagoev I, Kratchmarova S E, Ong M, Nielsen L J, Foster M Mann . A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling. Nature Biotechnology, 2003, 21( 3): 315– 318
14	E, Phizicky P I H, Bastiaens H, Zhu M, Snyder S Fields . Protein analysis on a proteomic scale. Nature, 2003, 422( 6928): 208– 215
15	Z H, Chen Z H, You L P, Li Z H, Guo P W, Hu H J Jiang. Combining LSTM network model and wavelet transform for predicting self-interacting proteins. In: Proceedings of the 15th International Conference on Intelligent Computing Theories and Application. 2019, 166– 174
16	C M Horejs . Good chemistry between proteins and materials. Nature Reviews Materials, 2019, 4( 7): 462– 462
17	W, Bao Z H, You D S Huang . CIPPN: computational identification of protein pupylation sites by using neural network. Oncotarget, 2017, 8( 65): 108867– 108879
18	Q, Huang Z, You X, Zhang Y Zhou . Prediction of protein–protein interactions with clustered amino acids and weighted sparse representation. International Journal of Molecular Sciences, 2015, 16( 5): 10855– 10869
19	Y A, Huang Z H, You X, Chen G Y Yan . Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition. BMC Systems Biology, 2016, 10( 4): 120
20	Y K, Lei Z H, You Z, Ji L, Zhu D S Huang . Assessing and predicting protein interactions by combining manifold embedding with multiple information integration. BMC Bioinformatics, 2012, 13( 7): S3
21	X, Luo Z, You M, Zhou S, Li H, Leung Y, Xia Q Zhu . A highly efficient approach to protein interactome mapping based on collaborative filtering framework. Scientific Reports, 2015, 5: 7702
22	L, Wang Z H, You S X, Xia X, Chen X, Yan Y, Zhou F Liu . An improved efficient rotation forest algorithm to predict the interactions among proteins. Soft Computing, 2018, 22( 10): 3373– 3381
23	Z H, You Y K, Lei J, Gui D S, Huang X Zhou . Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data. Bioinformatics, 2010, 26( 21): 2744– 2751
24	L, Zhu Z H, You D S Huang . Increasing the reliability of protein–protein interaction networks via non-convex semantic embedding. Neurocomputing, 2013, 121: 99– 107
25	J Y, An Z H, You X, Chen D S, Huang Z W, Li G, Liu Y Wang . Identification of self-interacting proteins by exploring evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix. Oncotarget, 2016, 7( 50): 82440– 82449
26	J Q, Li Z H, You X, Li Z, Ming X Chen . PSPEL: in silico prediction of self-interacting proteins from amino acids sequences using ensemble learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, 14( 5): 1165– 1172
27	Z, Liu F, Guo J, Zhang J, Wang L, Lu D, Li F He . Proteome-wide prediction of self-interacting proteins based on multiple properties. Molecular & Cellular Proteomics, 2013, 12( 6): 1689– 1700
28	Y A, Huang Z H, You X, Gao L, Wong L Wang . Using weighted sparse representation model combined with discrete cosine transformation to predict protein-protein interactions from protein sequence. BioMed Research International, 2015, 2015: 902198
29	L P, Li Y B, Wang Z H, You Y, Li J Y An . PCLPred: a bioinformatics method for predicting protein–protein interactions by combining relevance vector machine model with low-rank matrix approximation. International Journal of Molecular Sciences, 2018, 19( 4): 1029
30	Y B, Wang Z H, You X, Li T H, Jiang L, Cheng Z H Chen . Prediction of protein self-interactions using stacked long short-term memory from protein sequences information. BMC Systems Biology, 2018, 12( 8): 129
31	Z H, Zhan Z H, You Y, Zhou K, Zheng Z W Li. An efficient LightGBM model to predict protein self-interacting using Chebyshev moments and Bi-gram. In: Proceedings of the 15th International Conference on Intelligent Computing Theories and Application. 2019, 453– 459
32	UniProt Consortium The . UniProt: a worldwide hub of protein knowledge. Nucleic Acids Research, 2019, 47( D1): D506– D515
33	L, Salwinski C S, Miller A J, Smith F K, Pettit J U, Bowie D Eisenberg . The database of interacting proteins: 2004 update. Nucleic Acids Research, 2004, 32( S1): D449– D451
34	K, Breuer A K, Foroushani M R, Laird C, Chen A, Sribnaia R, Lo G L, Winsor R E W, Hancock F S L, Brinkman D J Lynn . InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation. Nucleic Acids Research, 2013, 41( D1): D1228– D1233
35	S, Orchard M, Ammari B, Aranda L, Breuza L, Briganti F, Broackes-Carter N H, Campbell G, Chavali C, Chen N, Del-Toro M, Duesbury M, Dumousseau E, Galeota U, Hinz M, Iannuccelli S, Jagannathan R, Jimenez J, Khadake A, Lagreid L, Licata R C, Lovering B, Meldal A N, Melidoni M, Milagros D, Peluso L, Perfetto P, Porras A, Raghunath S, Ricard-Blum B, Roechert A, Stutz M, Tognolli Roey K, Van G, Cesareni H Hermjakob . The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Research, 2014, 42( D1): D358– D363
36	R, Oughtred C, Stark B J, Breitkreutz J, Rust L, Boucher C, Chang N, Kolas L, O'Donnell G, Leung R, McAdam F, Zhang S, Dolma A, Willems J, Coulombe-Huntington A, Chatr-Aryamontri K, Dolinski M Tyers . The BioGRID interaction database: 2019 update. Nucleic Acids Research, 2019, 47( D1): D529– D541
37	O, Clerc M, Deniaud S D, Vallet A, Naba A, Rivet S, Perez N, Thierry-Mieg S Ricard-Blum . MatrixDB: integration of new data with a focus on glycosaminoglycan interactions. Nucleic Acids Research, 2019, 47( D1): D376– D381
38	X, Liu S, Yang C, Li Z, Zhang J Song . SPAR: a random forest-based predictor for self-interacting proteins with fine-grained domain information. Amino Acids, 2016, 48( 7): 1655– 1665
39	Ø D, Trier A K, Jain T Taxt . Feature extraction methods for character recognition-a survey. Pattern Recognition, 1996, 29( 4): 641– 662
40	I, Guyon S, Gunn M, Nikravesh L A Zadeh. Feature Extraction: Foundations and Applications. Springer, 2008
41	H, Li Y, Wei L, Li C L P Chen . Hierarchical feature extraction with local neural response for image recognition. IEEE Transactions on Cybernetics, 2013, 43( 2): 412– 424
42	I, Omara F, Li H, Zhang W Zuo . A novel geometric feature extraction method for ear recognition. Expert Systems with Applications, 2016, 65: 127– 135
43	W, Shao Y, Ding H B, Shen D Zhang . Deep model-based feature extraction for predicting protein subcellular localizations from bio-images. Frontiers of Computer Science, 2017, 11( 2): 243– 252
44	L, Wei P, Xing J, Zeng J, Chen R, Su F Guo . Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier. Artificial Intelligence in Medicine, 2017, 83: 67– 74
45	S F, Altschul E V Koonin . Iterated profile searches with PSI-BLAST—a tool for discovery in protein databases. Trends in Biochemical Sciences, 1998, 23( 11): 444– 447
46	R, Mosca A, Céol A, Stein R, Olivella P Aloy . 3did: a catalog of domain-based interactions of known three-dimensional structure. Nucleic Acids Research, 2014, 42( D1): D374– D379
47	R D, Finn A, Bateman J, Clements P, Coggill R Y, Eberhardt S R, Eddy A, Heger K, Hetherington L, Holm J, Mistry E L L, Sonnhammer J, Tate M Punta . Pfam: the protein families database. Nucleic Acids Research, 2014, 42( D1): D222– D230
48	R D, Finn J, Clements S R Eddy . HMMER web server: interactive sequence similarity searching. Nucleic Acids Research, 2011, 39( S2): W29– W37
49	I, Markovsky K Usevich . Software for weighted structured low-rank approximation. Journal of Computational and Applied Mathematics, 2014, 256: 278– 292
50	F, Zernike F J M Stratton . Diffraction theory of the knife-edge test and its improved form, the phase-contrast method. Monthly Notices of the Royal Astronomical Society, 1934, 94( 5): 377– 384
51	Y B, Wang Z H, You L P, Li D S, Huang F F, Zhou S Yang . Improving prediction of self-interacting proteins using stacked sparse auto-encoder with PSSM profiles. International Journal of Biological Sciences, 2018, 14( 8): 983– 991
52	J, Xu L, Xiang Q, Liu H, Gilmore J, Wu J, Tang A Madabhushi . Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Transactions on Medical Imaging, 2016, 35( 1): 119– 130
53	P F, Brown P V, Desouza R L, Mercer V J D, Pietra J C Lai . Class-based n-gram models of natural language. Computational Linguistics, 1992, 18( 4): 467– 479
54	J B, Mariño R E, Banchs J M, Crego Gispert A, de P, Lambert J A R, Fonollosa M R Costa-Jussà . N-gram-based machine translation. Computational Linguistics, 2006, 32( 4): 527– 549
55	S, Cao W, Lu J, Zhou X Li. cw2vec: learning Chinese word embeddings with stroke n-gram information . In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. 2018, 5053– 5061
56	M, Suzuki N, Itoh T, Nagano G, Kurata S Thomas. Improvements to n-gram language model using text generated from neural language model. In: Proceedings of ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2019, 7245– 7249
57	F R, Meng Z H, You X, Chen Y, Zhou J Y An . Prediction of drug–target interaction networks from the integration of protein sequences and drug chemical structures. Molecules, 2017, 22( 7): 1119
58	L, Zhang C, Zhang R, Gao R, Yang Q Song . Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes. BMC Bioinformatics, 2016, 17( 1): 225
59	B, Yu W, Qiu C, Chen A, Ma J, Jiang H, Zhou Q Ma . SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Bioinformatics, 2020, 36( 4): 1074– 1081
60	J W, Cooley P A W, Lewis P D Welch . The fast Fourier transform and its applications. IEEE Transactions on Education, 1969, 12( 1): 27– 34
61	M, Kapralov A, Velingker A Zandieh. Dimension-independent sparse Fourier transform. In: Proceedings of 2019 Annual ACM-SIAM Symposium on Discrete Algorithms. 2019, 2709– 2728
62	H J Nussbaumer. The fast Fourier transform. In: Nussbaumer H J, ed. Fast Fourier Transform and Convolution Algorithms. Berlin, Heidelberg: Springer, 1981, 80– 111
63	Z H, Chen Z H, You L P, Li Y B, Wang L, Wong H C Yi . Prediction of self-interacting proteins from protein sequence information based on random projection model and fast Fourier transform. International Journal of Molecular Sciences, 2019, 20( 4): 930
64	I, Babuška E, Vitásek F Kroupa . Some applications of the discrete Fourier transform to problems of crystal lattice deformation I. Cechoslovackij Fiziceskij Zurnal B, 1960, 10( 6): 419– 427
65	A V Anand. A brief study of discrete and fast Fourier transforms. The University of Chicago, Dissertation, 2010
66	D Sundararajan. Fourier Analysis—A Signal Processing Approach. Singapore: Springer, 2018
67	I Daubechies . The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 1990, 36( 5): 961– 1005
68	D Zhang. Wavelet transform. In: Zhang D, ed. Fundamentals of Image Data Mining. Cham: Springer, 2019, 35– 44
69	S Mallat . Zero-crossings of a wavelet transform. IEEE Transactions on Information Theory, 1991, 37( 4): 1019– 1033
70	C Q G, Muñoz A A, Jiménez F P G Márquez . Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis. Renewable Energy, 2018, 116: 42– 54
71	T, Chang C C J Kuo . Texture analysis and classification with tree-structured wavelet transform. IEEE Transactions on Image Processing, 1993, 2( 4): 429– 441
72	P, Abry S G, Roux H, Wendt P, Messier A G, Klein N, Tremblay P, Borgnat S, Jaffard B, Vedel J, Coddington L A Daffner . Multiscale anisotropic texture analysis and classification of photographic prints: art scholarship meets image processing algorithms. IEEE Signal Processing Magazine, 2015, 32( 4): 18– 27
73	A, Srinivasan P, Battacharjee A, Prasad G Sanyal. Brain MR image analysis using discrete wavelet transform with fractal feature analysis. In: Proceedings of the 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). 2018, 1660– 1664
74	D, Gupta S Choubey . Discrete wavelet transform for image processing. International Journal of Emerging Technology and Advanced Engineering, 2015, 4( 3): 598– 602
75	J, Chen Z, Li J, Pan G, Chen Y, Zi J, Yuan B, Chen Z He . Wavelet transform based on inner product in fault diagnosis of rotating machinery: a review. Mechanical Systems and Signal Processing, 2016, 70–71: 1– 35
76	Z H, Chen L P, Li Z, He J R, Zhou Y, Li L Wong . An improved deep forest model for predicting self-interacting proteins from protein sequence using wavelet transformation. Frontiers in Genetics, 2019, 10: 90
77	C C Tseng . Design of fractional order digital FIR differentiators. IEEE Signal Processing Letters, 2001, 8( 3): 77– 79
78	N, Sengupta N Kasabov . Spike-time encoding as a data compression technique for pattern recognition of temporal data. Information Sciences, 2017, 406–407: 133– 145
79	E S L, Gastal M M Oliveira . High-order recursive filtering of non-uniformly sampled signals for image and video processing. Computer Graphics Forum, 2015, 34( 2): 81– 93
80	P A, Haigh S T, Le S, Zvanovec Z, Ghassemlooy P, Luo T, Xu P, Chvojka T, Kanesan E, Giacoumidis P, Canyelles-Pericas H L, Minh W, Popoola S, Rajbhandari I, Papakonstantinou I Darwazeh . Multi-band carrier-less amplitude and phase modulation for bandlimited visible light communications systems. IEEE Wireless Communications, 2015, 22( 2): 46– 53
81	X, Shi H, Feng M, Zhai T, Yang B Hu . Infinite impulse response graph filters in wireless sensor networks. IEEE Signal Processing Letters, 2015, 22( 8): 1113– 1117
82	Z H, Chen Z H, You L P, Li Y B, Wang Y, Qiu P W Hu . Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter. BMC Genomics, 2019, 20( 13): 928
83	J, Chen T, Ma C Xiao. FastGCN: fast learning with graph convolutional networks via importance sampling. In: Proceedings of the 6th International Conference on Learning Representations, 2018
84	L, Wang Z H, You X, Yan K, Zheng Z W Li. GCNSP: a novel prediction method of self-interacting proteins based on graph convolutional networks. In: Proceedings of the 16th International Conference on Intelligent Computing Theories and Application. 2020, 109– 120
85	Z, Zeng S, Espino A, Roy X, Li S A, Khan S E, Clare X, Jiang R, Neapolitan Y Luo . Using natural language processing and machine learning to identify breast cancer local recurrence. BMC Bioinformatics, 2018, 19( 17): 498
86	V D, Badal P J, Kundrotas I A Vakser . Natural language processing in text mining for structural modeling of protein complexes. BMC Bioinformatics, 2018, 19( 1): 84
87	K, Yu T, Zhao P, Zhao J Zhang. Extraction of protein-protein interactions using natural language processing based pattern matching. In: Proceedings of 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2017, 1292– 1295
88	J, Lee W, Yoon S, Kim D, Kim S, Kim C H, So J Kang . BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 2020, 36( 4): 1234– 1240
89	Z H, Chen Z H, You W B, Zhang Y B, Wang L, Cheng D Alghazzawi . Global vectors representation of protein sequences and its application for predicting self-interacting proteins with multi-grained cascade forest model. Genes, 2019, 10( 11): 924
90	D, Wang P, Cui W Zhu. Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 1225– 1234
91	S, Chang W, Han J, Tang G J, Qi C C, Aggarwal T S Huang. Heterogeneous network embedding via deep architectures. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015, 119– 128
92	X, Wang P, Cui J, Wang J, Pei W, Zhu S Yang. Community preserving network embedding. In: Proceedings of the Thirty-first AAAI Conference on Artificial Intelligence. 2017, 203– 209
93	B, Perozzi R, Al-Rfou S Skiena. Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on KNOWLEDGE DISCOVERY and Data Mining. 2014, 701– 710
94	C, Tu W, Zhang Z, Liu M Sun. Max-margin deepwalk: discriminative learning of network representation. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. 2016, 3889– 3895
95	A, Shrestha M Won. DeepWalking: enabling smartphone-based walking speed estimation using deep learning. In: Proceedings of 2018 IEEE global communications conference (GLOBECOM). 2018, 1– 6
96	J, Tang M, Qu M, Wang M, Zhang J, Yan Q Mei. LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web. 2015, 1067– 1077
97	A, Grover J Leskovec. node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 855– 864
98	J, Peng J, Guan X Shang . Predicting Parkinson's disease genes based on node2vec and autoencoder. Frontiers in Genetics, 2019, 10: 226
99	S, Cao W, Lu Q Xu. Grarep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015, 891– 900
100	M, Ou P, Cui J, Pei Z, Zhang W Zhu. Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 1105– 1114
101	A, Galassi M, Lippi P Torroni . Attention in natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32( 10): 4291– 4308
102	K, Han A, Xiao E, Wu J, Guo C, Xu Y Wang. Transformer in transformer. In: Proceedings of the 35th Conference on Neural Information Processing Systems. 2021, 34
103	Q, Dai Q, Li J, Tang D Wang. Adversarial network embedding. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. 2018, 2167– 2174
104	S, Yuan X, Wu Y Xiang. SNE: signed network embedding. In: Proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. 2017, 183– 195
105	T, He K C C Chan . MISAGA: an algorithm for mining interesting subgraphs in attributed graphs. IEEE Transactions on Cybernetics, 2018, 48( 5): 1369– 1382
106	H, Liu H, Mao Y Fu. Robust multi-view feature selection. In: Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM). 2016, 281– 290
107	T, He Y, Liu T H, Ko K C C, Chan Y S Ong . Contextual correlation preserving multiview featured graph clustering. IEEE Transactions on Cybernetics, 2020, 50( 10): 4318– 4331
108	T, He L, Bai Y S Ong. Graph joint attention networks. 2021, arXiv preprint arXiv:2102.03147
109	T N, Kipf M Welling. Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations. 2016
110	V, Vapnik A Chervonenkis. A note on one class of perceptrons. Automation and Remote Control, 1964, 25: 821− 837
111	C J C Burges . A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2( 2): 121– 167
112	Y, Liu K, Wen Q, Gao X, Gao F Nie . SVM based multi-label learning with missing labels for image annotation. Pattern Recognition, 2018, 78: 307– 317
113	S, Tong D Koller . Support vector machine active learning with applications to text classification. The Journal of Machine Learning Research, 2002, 2: 45– 66
114	K, Kowsari D E, Brown M, Heidarysafa K J, Meimandi M S, Gerber L E Barnes. Hdltex: hierarchical deep learning for text classification. In: Proceedings of the 16th IEEE International Conference on Machine Learning and Applications (ICMLA). 2017, 364– 371
115	T Harris . Credit scoring using the clustered support vector machine. Expert Systems with Applications, 2015, 42( 2): 741– 750
116	S, Maldonado C, Bravo J, López J Pérez . Integrated framework for profit-based feature selection and SVM classification in credit scoring. Decision Support Systems, 2017, 104: 113– 121
117	P, Pławiak M, Abdar U R Acharya . Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring. Applied Soft Computing, 2019, 84: 105740
118	Z H, Chen Z H, You L P, Li Y B, Wang X Li. RP-FIRF: prediction of self-interacting proteins using random projection classifier combining with finite impulse response filter. In: Proceedings of the 14th International Conference on Intelligent Computing Theories and Application. 2018, 232– 240
119	X, Zhang S Liu . RBPPred: predicting RNA-binding proteins from sequence using SVM. Bioinformatics, 2017, 33( 6): 854– 862
120	G, Orlando D, Raimondi T, Khan T, Lenaerts W F Vranken . SVM-dependent pairwise HMM: an application to protein pairwise alignments. Bioinformatics, 2017, 33( 24): 3902– 3908
121	S, Huang N, Cai P P, Pacheco S, Narrandes Y, Wang W Xu . Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics & Proteomics, 2018, 15( 1): 41– 51
122	W W Hsieh. Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels. Cambridge: Cambridge University Press, 2009
123	M E Tipping. The relevance vector machine. In: Proceedings of the 12th International Conference on Neural Information Processing Systems. 1999, 652– 658
124	S, Kaltwang S, Todorovic M Pantic . Doubly sparse relevance vector machine for continuous facial behavior estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38( 9): 1748– 1761
125	H S, Karthik J Manikandan. Evaluation of relevance vector machine classifier for a real-time face recognition system. In: Proceedings of 2017 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). 2017, 26– 30
126	B, Demir S Erturk . Hyperspectral image classification using relevance vector machines. IEEE Geoscience and Remote Sensing Letters, 2007, 4( 4): 586– 590
127	B, Gholami W M, Haddad A R Tannenbaum . Relevance vector machine learning for neonate pain intensity assessment using digital imaging. IEEE Transactions on Biomedical Engineering, 2010, 57( 6): 1457– 1466
128	A, Widodo E Y, Kim J D, Son B S, Yang A C C, Tan D S, Gu B K, Choi J Mathew . Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Systems with Applications, 2009, 36( 3): 7252– 7261
129	T, Wang H, Xu J, Han E, Elbouchikhi Hachemi Benbouzid M El . Cascaded H-bridge multilevel inverter system fault diagnosis using a PCA and multiclass relevance vector machine approach. IEEE Transactions on Power Electronics, 2015, 30( 12): 7006– 7018
130	H, Mehrotra R, Singh M, Vatsa B Majhi . Incremental granular relevance vector machine: a case study in multimodal biometrics. Pattern Recognition, 2016, 56: 63– 76
131	L, Breiman A Cutler. State of the art of data mining using Random forest. In: Proceedings of the Salford Data Mining Conference, San Diego, USA. 2012, 24– 25
132	T K. Random decision forests Ho. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition. 1995, 278– 282
133	T K Ho . The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20( 8): 832– 844
134	T K Ho . A data complexity analysis of comparative advantages of decision forest constructors. Pattern Analysis & Applications, 2002, 5( 2): 102– 112
135	U M, Fayyad K B Irani. The attribute selection problem in decision tree generation. In: Proceedings of the 10th National Conference on Artificial Intelligence. 1992, 104– 110
136	J J, Rodriguez L I, Kuncheva C J Alonso . Rotation forest: a new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28( 10): 1619– 1630
137	J, Xia P, Du X, He J Chanussot . Hyperspectral remote sensing image classification based on rotation forest. IEEE Geoscience and Remote Sensing Letters, 2014, 11( 1): 239– 243
138	P, Du A, Samat B, Waske S, Liu Z Li . Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105: 38– 53
139	H, Lu L, Yang K, Yan Y, Xue Z Gao . A cost-sensitive rotation forest algorithm for gene expression data classification. Neurocomputing, 2017, 228: 270– 276
140	Z, Zhao Y, Shkolnisky A Singer . Fast steerable principal component analysis. IEEE Transactions on Computational Imaging, 2016, 2( 1): 1– 12
141	M Ringnér . What is principal component analysis?. Nature Biotechnology, 2008, 26( 3): 303– 304
142	S, Hochreiter J Schmidhuber. Long short-term memory. Neural Computation, 1997, 9(8): 1735– 1780
143	A Darmochwał . The Euclidean space. Formalized Mathematics, 1991, 2( 4): 599– 603
144	K X, Chiong M Shum . Random projection estimation of discrete-choice models with large choice sets. Management Science, 2019, 65( 1): 256– 271
145	T I, Cannings R J Samworth . Random-projection ensemble classification. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2017, 79( 4): 959– 1035
146	A, Schclar L Rokach. Random projection ensemble classifiers. In: Proceedings of the 11th International Conference on Enterprise Information Systems. 2009, 309– 316
147	M, Linial N, Linial N, Tishby G Yona . Global self-organization of all known protein sequences reveals inherent biological signatures. Journal of Molecular Biology, 1997, 268( 2): 539– 556
148	N, Goel G, Bebis A Nefian. Face recognition experiments with random projection. In: Proceedings of SPIE 5779, Biometric Technology for Human Identification II. 2005, 426– 437
149	C, Chen C M, Vong C M, Wong W, Wang P K Wong . Efficient extreme learning machine via very sparse random projection. Soft Computing, 2018, 22( 11): 3563– 3574
150	E J, Candes J, Romberg T Tao . Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, 52( 2): 489– 509
151	D L Donoho . Compressed sensing. IEEE Transactions on Information Theory, 2006, 52( 4): 1289– 1306
152	E, Bingham H Mannila. Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2001, 245– 250
153	J, Zhang M, Zhu P, Chen B Wang . DrugRPE: random projection ensemble approach to drug-target interaction prediction. Neurocomputing, 2017, 228: 256– 262
154	J, Jiang N, Wang P, Chen C, Zheng B Wang . Prediction of protein hotspots from whole protein sequences by a random projection ensemble system. International Journal of Molecular Sciences, 2017, 18( 7): 1543
155	H, Ge L, Sun Y, Yao J Yu. An automatic motif recognition algorithm in DNA sequences based on particle swarm optimization and random projection. In: Proceedings of 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2017, 2241– 2243
156	G E, Dahl J W, Stokes L, Deng D Yu. Large-scale malware classification using random projections and neural networks. In: Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. 2013, 3422– 3426
157	G, Hinton L, Deng D, Yu G, Dahl A R, Mohamed N, Jaitly A, Senior V, Vanhoucke P, Nguyen B Kingsbury . Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Processing Magazine, 2012, 29( 6): 82– 97
158	G E, Dahl D, Yu L, Deng A Acero . Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20( 1): 30– 42
159	D, Ciregan U, Meier J Schmidhuber. Multi-column deep neural networks for image classification. In: Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2012, 3642– 3649
160	C, Szegedy A, Toshev D Erhan. Deep neural networks for object detection. In: Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013, 2553– 2561
161	Y, LeCun Y, Bengio G Hinton . Deep learning. Nature, 2015, 521( 7553): 436– 444
162	I, Goodfellow Y, Bengio A Courville. Deep Learning. Cambridge: MIT Press, 2016
163	Z H, Zhou J Feng . Deep forest. National Science Review, 2019, 6( 1): 74– 86
164	L Breiman . Random forests. Machine Learning, 2001, 45( 1): 5– 32
165	T, Chen C Guestrin. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 785– 794
166	T, Chen T, He M, Benesty V, Khotilovich Y, Tang H, Cho K Chen. Xgboost: extreme gradient boosting. R package version 0.4–2, 2015, 1(4): 1– 4

[1]

FCS-21563-OF-ZC_suppl_1

Download

[1]	Xia-an BI, Yiming XIE, Hao WU, Luyun XU. Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm[J]. Front. Comput. Sci., 2021, 15(6): 156903-.
[2]	Yanbin WANG, Zhuhong YOU, Liping LI, Zhanheng CHEN. A survey of current trends in computational predictions of protein-protein interactions[J]. Front. Comput. Sci., 2020, 14(4): 144901-.
[3]	Yaru XIAN, Jun XIAO, Ying WANG. A fast registration algorithm of rock point cloud based on spherical projection and feature extraction[J]. Front. Comput. Sci., 2019, 13(1): 170-182.
[4]	Wei SHAO,Yi DING,Hong-Bin SHEN,Daoqiang ZHANG. Deep model-based feature extraction for predicting protein subcellular localizations from bio-images[J]. Front. Comput. Sci., 2017, 11(2): 243-252.
[5]	Fengying XIE,Yefen WU,Yang LI,Zhiguo JIANG,Rusong MENG. Adaptive segmentation based on multi-classification model for dermoscopy images[J]. Front. Comput. Sci., 2015, 9(5): 720-728.
[6]	Zhisong PAN,Zhantao DENG,Yibing WANG,Yanyan ZHANG. Dimensionality reduction via kernel sparse representation[J]. Front. Comput. Sci., 2014, 8(5): 807-815.
[7]	Yin LU, Fuxiang WANG, Xiaoyan LUO, Feng LIU. Novel infrared and visible image fusion method based on independent component analysis[J]. Front. Comput. Sci., 2014, 8(2): 243-254.
[8]	R PRIYA, T. N SHANMUGAM. A comprehensive review of significant researches on content based indexing and retrieval of visual information[J]. Front Comput Sci, 2013, 7(5): 782-799.
[9]	Jing WANG, Zhijing LIU, Hui ZHAO. A probabilistic model with multi-dimensional features for object extraction[J]. Front Comput Sci, 2012, 6(5): 513-526.
[10]	Tim SCHLüTER, Stefan CONRAD. An approach for automatic sleep stage scoring and apnea-hypopnea detection[J]. Front Comput Sci, 2012, 6(2): 230-241.
[11]	DAI Ruwei, XIAO Baihua, LIU Chenglin. Chinese character recognition: history, status and prospects[J]. Front. Comput. Sci., 2007, 1(2): 126-136.

Viewed

Full text

Abstract

Cited

Shared

Discussed