ArticleOpen Access

CLASSIFICATION OF TUMORS BASED ON GENETIC EXPRESSIONS

Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica Av. Luis Enrique Erro S/N, Unidad Profesional “Adolfo López Mateos” Edificio Z, Tercer Piso Zacatenco, Alcaldía Gustavo A. Madero, C.P. 07738, Ciudad de México, Mexico

Search for more papers by this author

SANDRA DINORA ORANTES-JIMÉNEZ

Instituto Politécnico Nacional，Centro de Investigación en Computación, Av. Juan de Dios Bátiz esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, Alcaldía Gustavo A. Madero C.P. 07738, Ciudad de México, Mexico

Search for more papers by this author

MARCO ANTONIO ACEVEDO-MOSQUEDA

Search for more papers by this author

, and

RICARDO CARREÑO AGUILERA

Universidad del Istmo, Campus Tehuantepec, Ciudad Universitaria S/N, Barrio Santa Cruz, 4a. Sección Sto. Domingo Tehuantepec, C.P. 70760, Oaxaca, Mexico

E-mail Address: ricardo.carreno.a@hotmail.com

Corresponding author.

Search for more papers by this author

https://doi.org/10.1142/S0218348X22501742Cited by:0 (Source: Crossref)

Abstract

This paper analyzes the ability of different machine learning algorithms to find patterns in the levels of gene expression for the correct classification of the five different types of tumors: breast, colon, kidney, lung, and prostate. The machine learning techniques were selected according to the most used algorithms in the related works: Bayesian method, Decision Trees, and $K$ -Nearest Neighbors. Three metrics were applied to test the performance of the classifiers: Precision, Recall, and $F_{1}$ -score. The results of Precision of the algorithms were 95.03% (Bayesian), 96.73% (Decision Trees), and 99.52% ( $K$ -Nearest Neighbors). A software prototype was developed to classify tumors based on genetic expressions utilizing these three algorithms with satisfactory results.

Publisher's Note:

There is an update on 2nd author’s affiliation. This information has been updated on 16th November 2022.

Keywords:

References

1. R. C. Chen et al., Selecting critical features for data classification based on machine learning methods, J. Big Data 7 (2020) 52. Crossref, Google Scholar
2. V. Sivalenka and A. Bai, An analysis on prediction of breast cancer using radius Nearest Neighbor algorithm over other classification algorithms, Mater. Today, Proc. (2021), https://doi.org/10.1016/j.matpr.2021.03.344. Crossref, Google Scholar
3. M. S. Ayyad, I. Ahmed and M. Labib, Gene expression cancer classification using modified K-Nearest Neighbors technique, Biosystems 176 (2019) 41–51. Crossref, Web of Science, Google Scholar
4. E. J. Shim et al., An MRI-based decision tree to distinguish lipomas and lipoma variants from well-differentiated liposarcoma of the extremity and superficial trunk: Classification and regression tree (CART) analysis, Eur. J. Radiol. 127 (2020) 109012. Crossref, Web of Science, Google Scholar
5. G. Murtaza, A. Wahab, G. Raza and L. Shuib, A tree-based multiclassification of breast tumor histopathology images through deep learning, Comput. Med. Imaging Graph. 89 (2021) 101870. Crossref, Web of Science, Google Scholar
6. G. S. Tandel, A. Tiwari and O. G. Kakde, Performance optimization of deep learning models using majority voting algorithm for brain tumor classification, Comput. Biol. Med. 135 (2021) 104564. Crossref, Web of Science, Google Scholar
7. D. Berrar, Bayes’ theorem and naive Bayes classifier, in Encyclopedia of Bioinformatics and Computational Biology, eds. S. Ranganathan, M. Gribskov, K. Nakai and C. Schönbach, Vol. 1 (Academic Press, Oxford, 2019), pp. 403–412. Crossref, Google Scholar
8. M. Abdulla and M. T. Khasawneh, G-Forest: An ensemble method for cost-sensitive feature selection in gene expression microarrays, Artif. Intell. Med. 108 (2020) 101941. Crossref, Web of Science, Google Scholar
9. C. Chamalidou et al., Survival patterns of invasive lobular and invasive ductal breast cancer in a large population-based cohort with two decades of follow up, Breast 59 (2021) 294–300. Crossref, Web of Science, Google Scholar
10. M. Wu et al., Enhanced expression of GABRD predicts poor prognosis in patients with colon adenocarcinoma, Transl. Oncol. 13 (2020) 100861. Crossref, Web of Science, Google Scholar
11. B. Wang, D. Chen and H. Hua, TBC1D3 family is a prognostic biomarker and correlates with immune infiltration in kidney renal clear cell carcinoma, Mol. Ther., Oncolytics 22 (2021) 528–538. Crossref, Web of Science, Google Scholar
12. F. Bray et al., Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J. Clin. 68(6) (2018) 394–424. Crossref, Web of Science, Google Scholar
13. D. J. Myers and J. M. Wallen, Lung Adenocarcinoma (StatPearls Publishing, Treasure Island, 2021). Google Scholar
14. American Cancer Society, Prostate cancer: Prostate cancer information and overview (s. f.) (2021), https://www.cancer.org/cancer/prostate-cancer.html. Google Scholar
15. C. Barbieri et al., Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer, Nat. Genet. 44 (2012) 685–689. Crossref, Web of Science, Google Scholar
16. D. Ferreira et al., Theory and practical use of Bayesian methods in interpreting clinical trial data: a narrative review, Br. J. Anaesth. 125(2) (2020) 201–207. Crossref, Web of Science, Google Scholar
17. L. Breiman, J. H. Friedman, R. A. Olshen and C. J. Stone, Classification and Regression Trees, 1st edn. (Wadsworth and Books, 1984), https://doi.org/10.1201/9781315139470. Google Scholar
18. M. Moshkov, Time complexity of decision trees, in Transactions on Rough Sets III, eds. J. F. Peters and A. Skowron, Lecture Notes in Computer Science, Vol. 3400 (Springer, Berlin, 2005), pp. 244–459. Crossref, Google Scholar
19. L. Rokach and O. Maimon, Data Mining with Decision Trees: Theory and Applications, World Scientific Series in Machine Perception and Artificial Intelligence, Vol. 69 (World Scientific, 2007). Link, Google Scholar
20. W. Li, Y. Chen and Y. Song, Boosted K-nearest neighbor classifiers based on fuzzy granules, Knowl.-Based Syst. 195 (2020) 105606. Crossref, Web of Science, Google Scholar
21. A. A. Alizadeh et al., Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature 403(6769) (2000) 503–511. Crossref, Web of Science, Google Scholar
22. P. García-Díaz, I. Sánchez-Berriel, J. A. Martínez-Rojas and A. M. Diez-Pascual, Unsupervised feature selection algorithm for multiclass cancer classification of gene expression RNA-Seq data, Genomics 112(2) (2020) 1916–1925. Crossref, Web of Science, Google Scholar
23. M. Lichman, UCI Machine Learning Repository (2013), School of Information and Computer Science, University of California, Irvine, CA, USA, https://archive.ics.uci.edu/ml/datasets/gene+expression+cancer+RNA-Seq. Google Scholar
24. C.-H. Hsu et al., Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning, Measurement 175 (2021) 109145. Crossref, Web of Science, Google Scholar
25. V. Lokeswari et al., Identification of common and dissimilar biomarkers for different cancer types from gene expressions of RNA-sequencing data, Gene Rep. 19 (2020) 100654. Crossref, Web of Science, Google Scholar
26. J. Priyank and S. Shriya, Analysis of different machine learning classifiers on MP election commission and breast cancer big dataset, Mater. Today, Proc. (2021), https://doi.org/10.1016/j.matpr.2021.04.248. Google Scholar
27. A. Kumar, P. Singh and M. Vardhan, Gene selection for cancer types classification using novel hybrid metaheuristics approach, Swarm Evol. Comput. 54 (2020) 100661. Crossref, Web of Science, Google Scholar
28. N. Goyal and M. Chandra, Breast cancer classification and identification using machine learning approaches, Mater. Today, Proc. (2020), https://doi.org/10.1016/j.matpr.2020.10.666. Crossref, Google Scholar
29. S. Roy, R. Kumar, V. Mittal and D. Gupta, Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning, Sci. Rep. 10(1) (2020) 4113. Crossref, Web of Science, Google Scholar
30. N. Al-Azzam and I. Shatnawi, Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer, Ann. Med. Surg. 62 (2021) 53–64. Crossref, Web of Science, Google Scholar
31. H. Zhang, The optimality of naive Bayes, in Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, Miami Beach, Florida, USA, 2004. Google Scholar
32. S. Shai and B. Shai, Nearest neighbor, in Understanding Machine Learning: From Theory to Algorithms (Cambridge University Press, 2014), pp. 258–267. Google Scholar
33. K. Murphy, Adaptive basis function models, in Machine Learning: A Probabilistic Perspective (The MIT Press, Cambridge, 2012), pp. 543–587. Google Scholar

Vol. 30, No. 07

Metrics

Downloaded 193 times

History

Received 6 June 2022

Accepted 11 August 2022

Published: October 19, 2022

Information

This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords

PDF download

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

CLASSIFICATION OF TUMORS BASED ON GENETIC EXPRESSIONS

Abstract

Recommended