Construction of Protein-Protein Interaction Network Using Community Molecular Detection

Authors(2) :-J. Monika, K. Srinivas

The number of proteins continues grow. Machine learning is a subfield of computer science that includes the study of systems that can learn from data, rather than follow only explicitly programmed instructions. Some of the most common techniques used for machine learning are Support Vector Machine, Artificial Neural Networks, k-Nearest Neighbor and Decision Tree. Machine learning techniques are widely used techniques in bioinformatics to solve different type of problems. In the year of 2014, the genome project was completed. Some of the proteins have an individual functionality. But there is no accurate information about function for remaining proteins and its network. In general, by using the In-Vitro and In-Vivo techniques are predict the functionality of proteins and its network. But the experimental investigation is costly and time consuming. To overcome this problem, In-silico technique was used such as molecular modeling, etc., but some limitation here is low accuracy. So here to construct Protein-Protein Interaction network for target protein. In this frame work, a novel technique is applied called Community Molecular Detection (CMD). Collect the dataset from “yeastExpData” package called litG. The CMD algorithm operates in two steps, first step is connected components, and second step is community prediction. The first step of CMD, find the connected components by using degree distribution. The second steps, molecular community prediction, takes the output of connected components graph and then find communities.

Authors and Affiliations

J. Monika
Computer Science Department, VR Siddhartha College, Student, Vijayawada, India
K. Srinivas
Computer Science Department, VR Siddhartha College, Student, Vijayawada, India

Support Vector Machine, Artificial Neural Networks, k-Nearest Neighbor, Decision Tree, Protein-Protein Interaction networks, communities.

  1. David L.González-Álvarez, Miguel A.Vega-Rodriguez,Alvaro Rubio-Largo, "Finding Patterns in Protein Sequences by Using a Hybrid Multiobjective Teaching Learning Based Optimization Algorithm",IEEE,vol .12,issue 3,2015
  2. Drees BL, Sundin B, Brazeau E, Caviston JP, Chen GC and Guo W, “A protein interaction map for cell polarity development”, 154:549-571, J CellBiol 2001.
  3. V Srinivasa Rao, K Srinivas, GN Sunand Kumar & GN Sujin “Protein interaction network for Alzheimer's disease using computational approach” BIOINFORMATION Volume 9(19) ISSN 0973-2063 page number:968-970
  4. Gary D Bader and Christopher WV Hogue “An automated method for finding molecular complexes in large protein interaction networks” BMC Bioinformatics 2003,Page Numbers:1-27
  5. K Srinivas ,R Kiran Kumar, M Mary Sujatha “A Study on Public Repositories of Human Protein Protein Interaction Data”, IJIACS, ISSN2347-8616 ,vol6-issue6, June2017.
  6. M Mary Sujatha, K. Srinivas, R. Kiran Kumar “A Review on Computational Methods Used in Construction of Protein Protein Interaction Network” International Journal of Engineering and Management Research Volume-6, Issue-6, Page Number: 71-77 November-December 2016
  7. M Wu, X.L. Li, C.K. Kwoh and S.K. Ng, "A Core-Attachment based Method to Detect Protein Complexes in PPI Networks," BMC Bioinformatics, vol. 10, pp. 169, 2009.
  8. J Susymary, R. Lawrance ,“Graph Theory Analysis of Protein-Protein Interaction Network and Clustering proteins linked with Zika Virus” Vol. 5, Special Issue 1, pp.100-108, 2017.
  9. vsrinivasa Rao,k.srinivas “Protein-Protein Interaction Detection: Methods and Analysis”, Hindawi Publishing Corporation International Journal of Proteomics Volume 2014, Article ID 147648, 12 pages
  10. M Mary Sujatha, K Srinivas “Pruning Protein Protein Interaction Network in Breast Cancer Data Analysis”, International Journal of Computer Science and Information Security (IJCSIS),Vol. 15, No. 7, July 2017.
  11. Ben Hur A, Ong CS, Sonnenburg S, Schölkopf B, Rätsch G. Support Vector Machines and Kernels for Computational Biology. PLoS.comput. biol. 2008;4(10):e1000173.
  12. Guo Y, Yu L, Wen Z, Li M. Using support vector machine combined with auto covariance to predict protein protein interactions from protein sequences. Nucleic Acids Res. 2008;36(9):3025–3030.
  13. Lo S, Cai C, Chen Y, Chung M. Effect of training datasets on support vector machine prediction of protein-protein interactions. Proteomics.2005;5(4):876 – 884.
  14. Dohkan S, Koike A, Takagi T. Improving the Performance of an SVM-Based Method for Predicting Protein-Protein Interactions. In. Silico Biol. 2006;6:515–529.
  15. Rashid M, Ramasamy S, PS Raghava G. A simple approach for predicting protein-protein interactions.Curr.Pro.Pept. Sci. 2010;11(7):589–6000.
  16. Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan N, Chung S, Emili A, Snyder M, Greenblatt J, Gerstein M. A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data. Science. 2003;302(5644):449 – 453.
  17. Chen X, Wang M, Zhang H. The use of classification trees for bioinformatics.Wiley Interdisciplinary Reviews. J of Data Mini and Know Disc. 2011;1(1):55–63.
  18. EA.Lan Liang, "MS-kNN: protein function prediction by integrating multiple data sources," BMC bioinformatics, 2014.
  19. Leicht EA, Holme P, Newman MEJ. Vertex similarity in networks. Physical ReviewE. 2006;73:026120.doi:10.1103/PhysRevE.73.026120. [doi:10.1103/PhysRevE.73.026120].
  20. Donoho, D. & Liu, R. (1988). The ‘automatic’ robustness of minimum distance functionals, Annals of Stat., Vol. 16,(1988), pp. 552-586, ISSN 0090-5364.
  21. Giet, L. & Lubrano, M. (2008). A minimum Hellinger distance estimator for stochastic differential equations: an application to statistical inference for continuous time interest rate models, Comput. Stat. & Data Anal., Vol. 52,No. 6, (Feb. 2008), pp. 2945-2965, ISSN: 0167-9473.
  22. Azim, G. A, Aboubekeur Hamdi-Cherif, Mohamed Ben Othman and Z.A. Abo-Eleneen” Protein Progressive MSA Using 2-Opt Method” Systems and Computational Biology- Bioinformatics and Computational Modeling 2011 InTech September 2011.

Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 2101-2112
Manuscript Number : CSEIT21833753
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

J. Monika, K. Srinivas, "Construction of Protein-Protein Interaction Network Using Community Molecular Detection", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.2101-2112, March-April-2018.
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