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Biased random walk with restart for essential proteins prediction |
| Pengli Lu(卢鹏丽)1,†, Yuntian Chen(陈云天)1, Teng Zhang(张腾)1, and Yonggang Liao(廖永刚)2 |
1 School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China;
2 China Mobile Communications Group Gansu Co., Ltd., Lanzhou 730070, China |
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Abstract Predicting essential proteins is crucial for discovering the process of cellular organization and viability. We propose biased random walk with restart algorithm for essential proteins prediction, called BRWR. Firstly, the common process of practice walk often sets the probability of particles transferring to adjacent nodes to be equal, neglecting the influence of the similarity structure on the transition probability. To address this problem, we redefine a novel transition probability matrix by integrating the gene express similarity and subcellular location similarity. The particles can obtain biased transferring probabilities to perform random walk so as to further exploit biological properties embedded in the network structure. Secondly, we use gene ontology (GO) terms score and subcellular score to calculate the initial probability vector of the random walk with restart. Finally, when the biased random walk with restart process reaches steady state, the protein importance score is obtained. In order to demonstrate superiority of BRWR, we conduct experiments on the YHQ, BioGRID, Krogan and Gavin PPI networks. The results show that the method BRWR is superior to other state-of-the-art methods in essential proteins recognition performance. Especially, compared with the contrast methods, the improvements of BRWR in terms of the ACC results range in 1.4%-5.7%, 1.3%-11.9%, 2.4%-8.8%, and 0.8%-14.2%, respectively. Therefore, BRWR is effective and reasonable.
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Received: 23 April 2022
Revised: 06 June 2022
Accepted manuscript online: 18 June 2022
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PACS:
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89.75.-k
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(Complex systems)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11861045 and 62162040). |
Corresponding Authors:
Pengli Lu
E-mail: lupengli88@163.com
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Cite this article:
Pengli Lu(卢鹏丽), Yuntian Chen(陈云天), Teng Zhang(张腾), and Yonggang Liao(廖永刚) Biased random walk with restart for essential proteins prediction 2022 Chin. Phys. B 31 118901
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[1] Kamath R, Fraser A, Dong Y, et al. 2003 Nature 421 231
[2] Winzeler E, Shoemaker D, Astromoff A, et al. 1999 Science 285 901
[3] Jeong H and Mason S 2001 Nature 411 41
[4] Gerardo J and Childs B 2001 Nature 409 853
[5] Nivit G, Shailendra S, Trilok A, et al. 2014 J. Comput. Biol. 21 456
[6] Giaever G, Chu A, Ni L, et al. 2002 Nature 418 387
[7] Cullen L, Arndt G, et al. 2005 Immunol. Cell Biol. 83 217
[8] Roemer T, Jiang B, Davison J, et al. 2003 Mol. Microbiol. 50 1
[9] Acencio M L and Lemke N 2009 BMC Bioinform. 10 290
[10] Karthik R, Nandita D and Govind K J 2014 Syst. Synth. Biol. 8 73
[11] Freeman L C 1978 Soc. Networks 1 215
[12] Joy M, Brock A, Ingber D, et al. 2005 J. Biotechnol. 2005 594674
[13] Estrada E and Juan A 2005 Physica A 364 581
[14] Bonacich P 1987 Am. J. Sociol. 92 1170
[15] Li M, Wang J X, Chen X, et al. 2011 Comput. Biol. Chem. 35 143
[16] Li M, Wang J X, Wang H, et al. 2012 IEEE ACM Trans. Comput. Biol. Bioinform. 9 1070
[17] Wang K L, Wu C X, Ai J, et al. 2019 Acta Phys. Sin. 68 196402 (in Chinese)
[18] Huang L Y, Huo Y L, Wang Q, et al. 2019 Acta Phys. Sin. 68 018901 (in Chinese)
[19] Wuchty S and Stadler P 2003 J. Theor. Biol. 223 45
[20] Hsing M, Byler K G and Cherkasov A 2008 BMC Syst. Biol. 2 80
[21] Li M, Ni P and Chen X 2017 IEEE Trans. Comput. Biol. Bioinformat. 16 1386
[22] Li M, Zhang H and Wang J X 2012 BMC Syst. Biol. 6 15
[23] Xiao Q H, Wang J X and Peng X, et al. 2015 BMC Genom. 16 (Suppl. 3) S1
[24] Li M and Lu Y 2017 IEEE Trans. Comput. Biol. Bioinformat. 14 380
[25] Zhang W, Xu J, Li X, et al. 2016 IEEE Trans. Nanobiosci. 15 939
[26] Luo J W and Qi Y 2015 PLoS One 10 e0131418
[27] Tang X, Wang J, Zhong J and Pan Y 2014 IEEE Trans. Comput. Biol. Bioinformat. 11 407
[28] Peng X, Wang J, Wu F X and Pan Y 2015 PLoS One 10 e0130743
[29] Zhou Y, Wu C and Tan L 2021 Physica A 570 125783
[30] Park H, Jung J and Kang U 2017 IEEE International Conference on Big Data, March 10-12, 2017, Beijing, China
[31] Jung J, Jin W, Sael L and Kang U 2016 IEEE 16th International Conference on Data Mining (ICDM), December 12-15, 2016, Barcelona, Spain, p. 973
[32] Zhou H J 2003 Phys. Rev. E 67 061901
[33] Zhou H J and Lipowsky R 2004 Lecture Notes in Computer Science (Berlin: Springer) Vol. 3038 pp. 1062-1069
[34] Bahadori S, Moradi P and Zare H 2020 Appl. Intell. 51 3561
[35] Bestehorn M, Riascos P and Michelitsch M 2021 Continuum Mechanics and Thermodynamics 33 1027
[36] Xu B, Guan J H, Wang Y and Wang Z W 2017 IEEE Trans. Comput. Biol. Bioinformat. 16 377
[37] Lv L S, Bardou D, Hu P, Liu Y Q and Yu G H 2022 Chaos Solitons Fractals 159
[38] Lovász L 2004 Lecture Notes in Mathematics (Berlin: Springer) Vol. 2 pp. 1-46
[39] Liu W, Ma L and Chen L 2020 J. Theor. Biol. 504 110414
[40] Zhu Z Q, Jin X L and Huang Z L 2012 Chin. Phys. Lett. 29 038901
[41] Lin L, Xu X, Ping H, et al. 2013 IEEE 10th Web Information System and Application Conference, November 10-15, 2013, Yangzhou, China, p. 281
[42] Lei X, Zhao J, Fujita H, et al. 2018 Knowl. Based Syst. 151 136
[43] Tong H H, Faloutsos C and Pan J Y 2007 IEEE Sixth International Conference on Data Mining (ICDM'06), June 25-28, 2006, Las Vegas, USA, pp. 613-622
[44] Razaghi-Moghadam Z, Abdollahi R, Goliaei S, et al. 2016 J. Biomed. Inform. 64 139
[45] Liu Z and Luo J 2017 Comput. Biol. Chem. 69 41
[46] Yu H, Greenbaum D, Lu H, Zhu X and Gerstein M 2004 Trends Genet. 20 227
[47] Stark C, Breitkreutz B J, Chatr-aryamontri A, et al. 2011 Nucleic Acids Res. 39 698
[48] Krogan N, Cagney G, Yu H, et al. 2006 Nature 440 637
[49] Gavin A C, Aloy P, Grandi P, et al. 2006 Nature 440 631
[50] Mewes H, Frishman D, Mayer K, et al. 2006 Nucleic Acids Res. 34 169
[51] Cherry J, Adler C, Ball C, et al. 1998 Nucleic Acids Res. 26 73
[52] Zhang R and Lin Y 2009 Nucleic Acids Res. 37 455
[53] Tu B P, Kudlicki A, Rowicka M and McKnight S L 2005 Science 310 1152
[54] Binder J X, Sune P F, Kalliopi T, et al. 2014 J. Biol. Databases Curation 2014 bau012
[55] Lei X, Yang X Q and Schreiber G 2018 PLoS One 13 e0198998 |
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