Support Vector Machine Implementation in the Analysis of HIV Species Based on Amino Acid and Dipeptide Sequences

Abstract: Infections are little irresistible specialists that can reproduce inside the cells in living life form. Human Immunodeficiency (HIV) is an infection that attacks human cells, debilitates the insusceptible framework and making individuals more vulnerable to disease. The favourable environment created by such virus allows grow of its content and rapidly increases within the hosts. HIV is a progressive sickness that causes a wide range of medical problems as a result of a widening array of bodily deficiencies and also considered as a lentivirus responsible for AIDS. The progressive breakdown of safe structures and the increase of dangerous breakthrough contamination characterise such human situation. In the study we used support vector machine (SVM) with Uniprot / Swiss-prot database to know the discrimination in HIV species based on Amino Acid and Dipeptide sequences to know the proportion and suggest the doctors to inject favourable protein to overcome the contamination. This also encourages the pharmaceutical industries to produce low cost medicines to cure of such diseases and also allow the patients to recover from diseases in low cost.

Support Vector Machine, HIV, Insusceptible Framework, Progressive sickness

1. Ardekani A.M. and Naeini M.M. “The role of miRNA in human diseases”, Avicenna J Med Biotech, vol 2, no.4, pages 161-179, (2010). [Google Scholar] [Crossref]

2. Asa Ben- Hur, Cheng Soon Ong, “Support Vector Machines and Kernels for Computational Biology”. 2009. [Google Scholar] [Crossref]

3. Bickel S, Bogojeska J, Lengauer J, Schiffer T., “Multi-Task Learning for HIV Therapy Screening”, Appearing in Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, (2010). [Google Scholar] [Crossref]

4. Carr JK, Foley BT, Leitner T, Salminen M, Korber B, McCutchan F, "Reference sequences representing the principal genetic diversity of HIV-1 in the pandemic”. In Los Alamos National Laboratory (ed.), HIV sequence compendium, pp. 10–19, (1998). [Google Scholar] [Crossref]

5. Caruana, R. and Niculescu-Mizil, A.: "An empirical comparison of supervised learning algorithms", Proceedings of the 23rd international conference on Machine learning, (2006). [Google Scholar] [Crossref]

6. Chang, C.-C., Hsu C.W. & Lin, C.-J., “A practical guide to support vector classification”, 2010. [Google Scholar] [Crossref]

7. Dolin, [edited by] Gerald L. Mandell, John E. Bennett, Raphael. Mandell, Douglas, and Bennett's principles and practice of infectious diseases (7th ed.). Philadelphia, PA: Churchill Livingstone/Elsevier. pp. Chapter 169, (2010). [Google Scholar] [Crossref]

8. Hanbing Rao, Guobing Yang, Ningxin Tan, Ping Li, Zerong Li, Xiangyuan Li, “Prediction of HIV-1 Protease Inhibitors Using Machine Learning Approaches”, QSAR & Combinatorial Science, vol. 28, Issue 11-12, pages 1346–1357, (2009). [Google Scholar] [Crossref]

9. Heider, D, Verheyen J. and Hoffmann D. Machine learning on normalized protein sequences”, BMC Research Notes, vol. 4, no.94 (2011). [Google Scholar] [Crossref]

10. Keele, B.; Jones, J.; Terio, K.; Estes, J.; Rudicell, R.; Wilson, M.; Li, Y.; Learn, G. et al. "Increased mortality and AIDS-like immunopathology in wild chimpanzees infected with SIVcpz". Nature, vol.460, no.7254, pages 515–519, (2009). [Google Scholar] [Crossref]

11. Klon AE, Glick M and Davies JW, “Applications of machine learning to improve the results of high-throughput docking against the HIV-1 protease”, J Chem Inf Comput Sci., Vol.44, no.6, pages 2216-2224, (2010). [Google Scholar] [Crossref]

12. Kitahata MM, Gange SJ, Abraham AG, et al., "Effect of early versus deferred antiretroviral therapy for HIV on survival". N. Engl. J. Med. Vol.360, no. 18, pages 1815–26. (2009), [Google Scholar] [Crossref]

13. Lakhashe S.,Thakar M,Godbole S, Tripathy S. and Paranjape R. ,” HIV Infection in India: Epidemiology and Pathogenesis.” National AIDS Research Institute Pune, INDIA, Journal of Biosciences, vol. 33, no. pages515-25. (2008). [Google Scholar] [Crossref]

14. Los Alamos National Laboratory Established 1943. "Fighting the world’s most dangerous disease, Los Alamos Lab". Lanl.gov. (2010). [Google Scholar] [Crossref]

15. Philip E.Bourne, Helge Weissig, San Diego, Structural bioinformatics, University of California, San Diego. Wiley-liss publication, (2009). [Google Scholar] [Crossref]

16. Plantier JC, Leoz M, Dickerson JE, Oliveira F.D., Cordonnier F., Véronique Lemée V., Damond F., Robertson D.L., & Simon F., "A new human immunodeficiency virus derived from gorillas", Nature Medicine, vol.15, no.8, pages 871–872, (2009). [Google Scholar] [Crossref]

17. Prosperi MC, “Robust supervised and unsupervised statistical learning for HIV type 1 coreceptor usage analysis”, AIDS Res Human retroviruses, vol.25, no.3, pages 305-14, (2009). [Google Scholar] [Crossref]

18. Ratha B.S. and Patwal P. S., “An Evaluation of the HIV Vector Machine Classification Study”, Airo International Journal, Vol. 3, Issue 2, pages 570-577(2023). [Google Scholar] [Crossref]

19. Ratha B.S. and Patwal P. S., “An Examination of the Effects of HIV on Permanent System”, Airo National Journal, Vol. 3, Issue 2, pages 140-145(2023). [Google Scholar] [Crossref]

20. Rishishwar L., Mishra N., Pant B., Pant K., and Pardasani K.R., “ProCoS: Protein composition server”, Bioinformation, vol.5, no.5, 227 (2010) [Google Scholar] [Crossref]

21. Septiembre J., “Transmembrane glycoproteins cross reactive HIV-1/HIV-2 epitope”, Rev Biomed, vol.11, No. 3, pages 155-160, (2000). [Google Scholar] [Crossref]

22. Sharp, PM; Hahn, BH, “Origins of HIV and the AIDS Pandemic", Cold Spring Harbor perspectives in medicine 1 (1): a006841, (2011). [Google Scholar] [Crossref]

23. Singh Y. and Mars M. “Support vector machines to forecast changes in CD4 count of HIV-1 positive patients”, Scientific Research and Essays, vol. 5, no.17, pages 2384-2390, (2010). [Google Scholar] [Crossref]

24. Söllner J, Mayer B, “Machine learning approaches for prediction of linear B-cell epitopes on proteins”, J Mol Recognition, vol.19, no.3, pages 200-208, (2006). [Google Scholar] [Crossref]

25. Song C. and Shi F., “Prediction of Subcellular Localization of Apoptosis Proteins by Dipeptide Composition”, International Journal of Digital content Technology and its Applications, vol. 4, no. 1, pages 32-36, (2010). [Google Scholar] [Crossref]

26. Tierney, A.J., “Structure and function of invertebrate 5-HT receptors: a review” Comp. Biochem. Physiol. A Mol. Integr. Physiol., vol.128, pages 791–804, (2001). [Google Scholar] [Crossref]

27. Translocon-Assisted Membrane Protein Folding from the Stephen White laboratory at UC Irvine, (2008). [Google Scholar] [Crossref]

28. White S.H. and Heigne G.V. “How translocon selects transmembrane helices?”, Annual review of Biophysics, vol. 37, pages 23-42, (2008). [Google Scholar] [Crossref]

29. Yavuz O. and Ozyilmaz L., “Analysis and Classification of Hiv-1 Sub-Type Viruses by AR Model through Artificial Neural Networks”, World Academy of Science, Engineering and Technology, vol. 49, pages 826-831, (2009). [Google Scholar] [Crossref]

30. Zazzi M et al. “Predicting response to antiretroviral treatment by machine learning”, The EuResist project, Intervirology, vol.55, no.2, pages 123-127, (2012). [Google Scholar] [Crossref]

31. http:// www.pdb.org [Google Scholar] [Crossref]

32. http://bioweb.pasteur.fr/seqanal/interfaces/topagesred.html [Google Scholar] [Crossref]

33. http://www.cbs.dtu.dk/services/TMHMM/or [Google Scholar] [Crossref]

34. http://www.cbs.dtu.dk/services/TMHMM-2.0/ [Google Scholar] [Crossref]

35. http://www.dtreg.com/svm.html [Google Scholar] [Crossref]

36. http://www.csbio.sjtu.edu.cn/bioinf/virus [Google Scholar] [Crossref]

37. http://www.niaid.nih.gov [Google Scholar] [Crossref]

38. http://www.uniprot.org [Google Scholar] [Crossref]

39. http://www.misangar.ac.uk/ microRNA registry [Google Scholar] [Crossref]

40. http://www.yale.edu/bio243/HIV/genome.html [Google Scholar] [Crossref]

41. http://i_base.info/qa/36 [Google Scholar] [Crossref]

• Patriarchy and Gender Roles in Shobhaa De's Second Thoughts and Easterine Kire’s Life on Hold
• Comparative Effects of Zeolite and Fly Ash on Soil Properties and Agricultural Productivity
• Assessing Interrelationships among Banking Satisfaction, Cooperative Perspectives and Rural Service Beliefs in Cooperative Banking in Northern Nigeria
• High-Performance Alloys and Composites’ Applications in Production Engineering
• Integrating Tidal Energy into Sub-Saharan Africa’s Power Mix: A Strategic Framework for Renewable Energy Expansion