Optimization of Machine Learning Algorithms for Proteomic Analysis Using TOPSIS

Author(s): Javanbakht T.1*, Chakravorty S.2

Affiliation(s):
1 Department of Computer Science, University of Quebec in Montreal, 201, President Kennedy St., Montreal, Quebec H2X 3Y7, Canada;
2 Biju Patnaik University of Technology, Chhend Colony, Rourkela, Odisha 769004, India

*Corresponding Author’s Address: [email protected]

Issue: Volume 9, Issue 2 (2022)

Dates:
Submitted: August 21, 2022
Accepted for publication: November 24, 2022
Available online: November 28, 2022

Citation:
Javanbakht T., Chakravorty S. (2022). Optimization of machine learning algorithms for proteomic analysis using topsis. Journal of Engineering Sciences, Vol. 9(2), pp. E7-E11, doi: 10.21272/jes.2022.9(2).e2

DOI: 10.21272/jes.2022.9(2).e2

Research Area:  MECHANICAL ENGINEERING: Computational Mechanics

Abstract. The present study focuses on a new application of the TOPSIS method for the optimization of machine learning algorithms, supervised neural networks (SNN), the quick classifier (QC), and genetic algorithm (GA) for proteomic analysis. The main hypotheses are that the change in the weights of alternatives could affect the ranking of algorithms. The obtained data confirmed this hypothesis for their ranking. Moreover, adding labor as a cost criterion to the list of criteria did not affect this ranking. This was because candidate 3 had better fuzzy membership degrees than the two other candidates concerning their criteria. This work showed the importance of the value of the fuzzy membership degrees of the cost criterion of the algorithms in their ranks. The values of the fuzzy membership degrees of the algorithms used for proteomic analysis could determine their priority according to their score differences. One of the advantages of this study was that the studied methods could be compared according to their characteristics. Another advantage was that the obtained results could be related to the new ones after improving these methods. The results of this work could be applied in engineering, where the analysis of proteins would be performed with these methods.

Keywords: multi-criteria decision making, TOPSIS, prediction, proteomic analysis.

References:

  1. Balioti, V., Tzimopoulos, C., Evangelides, C. (2018). Multi-criteria decision making using TOPSIS method under fuzzy environment. Application in spillway selection. Proceedings, 2, 637. https://doi.org/10.3390/proceedings2110637.
  2. Tlas, M., Ghani, B. A. (2020). Interactive software for classification and ranking procedures based on multi-criteria decision-making algorithms. Computational Ecology and Software, 10(3), 133–150.
  3. Rohanah, S. (2018). An evaluation of students performance using TOPSIS and Entropy approaches. IOSR Journal of Research, Method in Education, 8(6), 1–6. https://doi.org/10.9790/7388-0806010106.
  4. Sałabun, W., Wątróbski, J., Shekhovtsov, A. (2020). Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry, 12(9), 1549. https://doi.org/10.3390/sym12091549.
  5. Chang, S. – H., Tseng, H.-E. (2008). Fuzzy Topsis decision method for configuration management. International Journal of Industrial Engineering, 15(3), 304-313. https://doi.org/10.23055/ijietap.2008.15.3.147.
  6. Bulgurcu, B. (2012). Application of TOPSIS technique for financial performance evaluation of technology firms in Istanbul stock exchange market. Procedia, 62, 1033-1040. https://doi.org/10.1016/j.sbspro.2012.09.176.
  7. Kochkina, M. V., Karamyshev, A. N., Isavnin, A. G. (2017). Modified multi-criteria decision making method development based on “AHP” and “TOPSIS” methods using probabilistic interval estimates. The Turkish Online Journal of Design, Art and Communication TOJDAC, 1663-1674. https://doi.org/10.7456/1070DSE/144.
  8. Abidin, M. Z., Rusli, R., Shariff, A. M. (2016). Technique for order performance by similarity to ideal solution (TOPSIS)- entropy methodology for inherent safety design decision making tool, Procedia Engineering, 148, 1043-1050. https://doi.org/10.1016/j.proeng.2016.06.587.
  9. Álvarez, J. V., Bravo, S. B., Chantada-Vázquez, M. P., Barbosa-Gouveia, S., Colón, C., López-Suarez , O., Tomatsu, S., Otero-Espinar, F. J., Couce, M. L. (2021). Plasma proteomic analysis in Morquio A disease. International Journal of Molecular sciences, 22, 6165. https://doi.org/10.3390/ijms22116165.
  10. Balbao, E., Marín, T., Oyarzún, J. E., Contreras, P. S., Hardt, R., van den Bosch, T., Alvarez, A. R., Rebolledo-Jaramillo, B., Klein, A. D., Winter, D., Zanlungo, S. (2021). Proteomic analysis of Niemann-pick type C hepatocytes reveals potential therapeutic targets for liver damage, Cells, 10, 2159. https://doi.org/10.3390/cells10082159.
  11. Chen, Y., Yao, H., Zhang, N., Wu, J., Gao, S., Guo, J., Lu, X., Cheng, L., Luo, R., Liang, X., Wong, C. C. L., Zheng, M. (2021). Proteomic analysis identifies prolonged disturbances in pathways related to cholesterol metabolism and myocardium function in the COVID-19 recovery stage. Journal of Proteome Research, 20, 7, 3463-3474. https://doi.org/10.1021/acs.jproteome.1c00054.
  12. Filbin, M. R., Mehta, A., Schneider, A. M., Kays, K. R., Guess, J. R., Gentili, M., Fenyves, B. G., Charland, N. C., Gonye, A. L. K., Gushterova, I., Khanna, H. K., LaSalle, T. J., Lavin-Parsons, K. M., Lilley, B. M., Lodenstein, C. L., et al. (2021). Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions. Cell Reports Medicine, 2, 100287. https://doi.org/10.1016/j.xcrm.2021.100287.
  13. Swiatly, A., Horala, A., Hajduk, J., Matysiak, J., Nowak-Markwitz, E., Kokot, Z. J. (2017). MALDI-TOF-MS analysis in discovery and identification of serum proteomic patterns of ovarian cancer. BMC Cancer, 17, 472, 1–9.
  14. Saghapour, E., Kermani, S., Sehhati, M. (2017). A novel feature ranking method for prediction of cancer stages using proteomics data. Plos One, 12(9), e0184203. https://doi.org/10.1371/journal.pone.0184203.
  15. Mallik, S., Zhao, Z. (2019). Multi-objective optimized fuzzy clustering for detecting cell clusters from single-cell expression profiles. Genes, 10(8), 611. https://doi.org/10.3390/genes10080611.
  16. Torng, W. Altman, R. B. (2017). 3D deep convolutional neural networks for amino acid environment similarity analysis. BMC Bioinformatics, 18, 302. https://doi.org/10.1186/s12859-017-1702-0.
  17. Muzio, G., O’Bray, L., Borgwardt, K., Notes, A. (2021). Biological network analysis with deep learning. Briefings in Bioinformatics, 22(2), 1515-1530. https://doi.org/10.1093/bib/bbaa257.
  18. Chiu, C. -Y., Hsieh, S. -Y., Wong, K. -S., Lai, S. -H., Chen, J. -K., Huang, J. -L. (2015). The value of total protein in guiding management of infectious parapneumonic effusion by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Journal of Microbiology, Immunology and Infection, 48(5), 483-489. https://doi.org/10.1016/j.jmii.2013.11.013.
  19. Fan, N. -J., Gao, C. -F., Wang, X. -L., Zhao, G., Liu, Q. -Y., Zhang, Y. -Y., Cheng, B. -G. (2012). Serum peptidome patterns of colorectal cancer based on magnetic bead separation and MALDI-TOF mass spectrometry analysis. J. Biomed. Biotechnol., 2012, 985020. https://doi.org/10.1155/2012/985020.
  20. Unger, R. (2004). The Genetic Algorithm Approach to Protein Structure Prediction. Struncture and Bonding, 110, 2697-2699. https://doi.org/10.1007/b13936.
  21. Lv, Y. et al. (2006). Improved Genetic Algorithm for Multiple Sequence Alignment Using Segment Profiles (GASP). International Conference on Advanced Data Mining and Applications, In Lecture notes in computer science, 4093, 388-395. https://doi.org/10.1007/11811305_43.
  22. Javanbakht, T., Chakravorty, S. (2022). Prediction of human behavior with TOPSIS. Fuzzy Extension and Applications, 3(2), 109-125. https://doi.org/10.22105/jfea.2022.326185.1197.
  23. Bark, S. J., Hook, V. (2007). The future of proteomic analysis in biological systems and molecular medicine. Mol. Biosyst., 3(1), 14-17. https://doi.org/10.1039/b611446.
  24. Dastmalchi, M., Dhaubhadel, S. (2015). Proteomic insights into synthesis of isoflavonoids in soybean seeds. Proteomics, 15, 10, 1646-1657. https://doi.org/10.1002/pmic.201400444.
  25. Spitzer, A. R., Chace, D. (2008). Proteomics- and metabolomics-based neonatal diagnostics in assessing and managing the critically Ill neonate. Clinic in Prinatology, 35(4), 695-716. https://doi.org/10.1016/j.clp.2008.07.019.
  26. Conrads, T. P., Fusaro, V. A., Ross, S., Johann, D., Rajapakse, V., Hitt, B. A., Steinberg, S. M., Kohn, E. C., Fishman, D. A., Whitely, G., Barrett, J. C., Liotta, L. A., Petricoin, E. F., Veenstra, T. D. (2204). High-resolution serum proteomic features for ovarian cancer detection, Endoctrine-Related Cancer, 11, 163-178. https://doi.org/10.1677/erc.0.0110163.
  27. Javanbakht, T., David, E. (2020). Rheological and physical properties of a nanocomposite of graphene oxide nanoribbons with polyvinyl alcohol. Journal of Thermoplastic Composite Materials, 0892705720912767. https://doi.org/10.1177/0892705720912767.
  28. Javanbakht, T., Laurent, S., Stanicki, D., Frenette, M. (2020). Correlation between physicochemical properties of superparamagnetic iron oxide nanoparticles and their reactivity with hydrogen peroxide. Canadian Journal of Chemistry, 98, 601-608. https://doi.org/10.1139/cjc-2020-0087.
  29. Javanbakht, T., Sokolowski, W. (2015). Thiol-ene/acrylate systems for biomedical shape-memory polymers. Shape Memory Polymers for Biomedical Applications, 157-166. https://doi.org/10.1016/B978-0-85709-698-2.00008-8.
  30. Javanbakht, T., Ghane-Motlagh, B., Sawan, M. (2020). Comparative study of antibiofilm activity and physicochemical properties of microelectrode arrays. Microelectronic Engineering, 229, 111305. https://doi.org/10.1016/j.mee.2020.111305.
  31. Pakpour, S., Olishevska, S., Prasher, S., Milani, A. S., Chénier, M. R. (2013). DNA extraction method selection for agricultural soil using TOPSIS multiple criteria decision-making model. American Journal of Molecular Biology, 3, 215-228. https://doi.org/10.4236/ajmb.2013.34028.
  32. Tripathy, J., Dash, R., Pattanayak, B. K., Mishra, S. K., Mishra, T. K., Puthal, D. (2022). Combination of reduction detection using TOPSIS for gene expression data analysis, Big Data and Cognitive Computing, 6(1), 24. https://doi.org/10.3390/bdcc6010024.
  33. Singh, S., Li, H. (2021). Comparative study of bioinformatic tools for the identification of chimeric RNAs from RNA Sequencing, RNA Biology, 18, S1, 254-267. https://doi.org/10.1080/15476286.2021.1940047.
  34. Djavanbakht, T., Carrier, V., André, J. M., Barchewitz, R., Troussel, P. (2000). Effets d’un chauffage thermique sur les performances de miroirs multicouches Mo/Si, Mo/C et Ni/C pour le rayonnement X mou. Journal de Physique IV, France, 10, 281-287. https://doi.org/10.1051/jp4:20001031.
  35. Krishnamoorthy, K., Mahalingam, M. (2015). Selection of a suitable method for the preparation of polymeric nanoparticles: Multi-criteria decision making approach. Advanced Pharmaceutical Bulletin, 5(1), 57-67. https://doi.org/10.5681/apb.2015.008.
  36. Javanbakht, T., Hadian, H., Wilkinson, K. J. (2020). Comparative study of physicochemical properties and antibiofilm activity of graphene oxide nanoribbons. Journal of Engineering Sciences, 7(1), C1-C8. https://doi.org/10.21272/jes.2020.7(1).c1.
  37. Negi, R. S., Bisht, R. S., Singh, R. K., Prasad, L. (2019). Physico-mechanical and antibacterial properties of pine gum/epoxy composites with/without silver nanoparticles. Marcomolecular Materials and Engineering, 304, 7, 1800744. https://doi.org/10.1002/mame.201800744.

Full Text