Abstract
Today, with the development of technology, the decision-making capabilities of machines have also increased. With their high analytical skills, computers can easily catch points and relationships that may escape the human eye.
Thanks to these capabilities, machines are also widely used in the field of health. For example, many machine-learning techniques developed on cancer prediction have been successfully applied. Early detection of cancer is crucial to survival. In the early diagnosis of cancer, the rates of drug treatment, chemotherapy, or radiotherapy that the person will be exposed to are significantly reduced and the patient gets through this process with the least amount of wear and tear. Gene Expression Cancer RNA-Seq Dataset was used in this study. This data set includes gene expression values of 5 cancer types (BRCA, KIRC, LUAD, LUSC, UCEC). DNA sequences in the dataset were analyzed using k-means and hierarchical clustering algorithms, which are unsupervised machine learning methods. The aim of the study is to develop a usable machine-learning model for the early detection of cancer at the gene level. Adjusted Rand Index (ARI), Silhouette Score, and Accuracy Metrics were used to evaluate the analysis results. The rand index calculates the similarity between clusters by counting the binaries assigned to clusters. The adjusted Rand Index is a randomly adjusted version of the Rand Index. The silhouette score indicates how well a data point fits within its own set among separated datasets. The accuracy metric is obtained as a percentage of correctly clustered data points divided by all predictions. Different connection methods are used in the hierarchical clustering algorithm. These are 'complete', 'ward', 'average', and 'single'. As a result of the study, the accuracy in the k-means algorithm was 0.990, the Adjusted Rand Index was 0.79, and the Silhouette Score was 0.14. Looking at the hierarchical clustering, ward performed the best of the four linkage methods, with an ARI score of 0.76 and a silhouette score of 0.13. As a result of the study, the accuracy of the hierarchical clustering algorithm was 0.999.
Keywords
k-means Machine Learning Unsupervised Learning Cancer DNA DNA Sequences Hierarchical Clustering
Supporting Institution
Bursa Teknik Üniversitesi
Thanks
The authors are grateful to TUBITAK ULAKBIM High Performance and Grid Computing Center (TRUBA), Bursa Technical University High-Performance Computing Laboratory.
References
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Abstract
References
- Prat A , Pineda E,Adamo B, Galván P, Fernández A, Gaba L, et al. Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast, 2015
- M.C. de Souto, I.G. Costa, D.S. de Araujo, T.B. Ludermir, A. Schliep, Clustering cancer gene expression data: a comparative study, BMC Bioinforma. 9 (1) (2008) 497, https://doi.org/10.1186/1471-2105-9-497
- S. Saha, A. Ekbal, K. Gupta, S. Bandyopadhyay, Gene expression data clustering using a multiobjective symmetry based clustering technique, Comput. Biol. Med. 43 (11) (2013) 1965–1977, https://doi.org/10.1016/j.compbiomed.2013.07.021
- Fahad Hussain, Umair Saeed, Ghulam Muhammad, Noman Islam and Ghazala Shafi Sheikh, “Classifying cancer patients based on DNA sequences using machine learning”, 2019
- Elaheh Moradi, Antonietta Pepe, Christian Gaser, Heikki Huttunen, Jussi Tohka, “Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects”, 2014
- KonstantinaKourou, Themis P.Exarchos, Konstantinos P. Exarchos, Michalis V. Karamouzis, Dimitrios I. Fotiadisa, “Machine learning applications in cancer prognosis and prediction”, Computational and Structural Biotechnology Journal, 2015
- Gunasekaran Manogaran, V. Vijayakumar R. Varatharajan, Priyan Malarvizhi Kumar, Revathi Sundarasekar, Ching-Hsien Hsu, “Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering”, Wireless Personal Communications, 2018
- Zeid Khitan, Anna P. Shapiro, Preeya T. Shah, Juan R. Sanabria, Prasanna Santhanam, Komal Sodhi, Nader G. Abraham, and Joseph I. Shapiro, “Predicting Adverse Outcomes in Chronic Kidney Disease Using Machine Learning Methods: Data from the Modification of Diet in Renal Disease”, Marshall Journal of Medicine, 2017
- Manish Motwani, Damini Dey, Daniel S. Berman, Guido Germano, Stephan Achenbach et al., “Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5- year multicentre prospective registry analysis”, European Heart Journal, 2017
- Timor Kadir, Fergus Gleeson, “Lung cancer prediction using machine learning and advanced imaging Techniques”, 2018
- Md. Milon Islam, Md. Rezwanul Haque, Hasib Iqbal, Md. Munirul Hasan, Mahmudul Hasan, Muhammad Nomani Kabir, “Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques”, 2020
- Yixuan Li , Zixuan Chen, “Performance Evaluation of Machine Learning Methods for Breast Cancer Prediction”, 2018
- Mingyang Lu, Zhenjiang Fand, Bin Xu, Lujun Chen, Xiao Zheng, Jundong Li, Taieb Znati, Qi Mi, Jingting Jiang, “Using machine learning to predict ovarian cancer”, 2020
- Imad Dabbura, "K-Means Clustering: Algorithm, Applications, Evaluation Methods, and Drawbacks" 2018, <https://towardsdatascience.com/k-means-clustering-algorithm-applications-evaluation-methods-and-drawbacks-aa03e644b48a>, (01 June 2022)
- Dr. Michael J. Garbade, "Understanding K-Means Clustering in Machine Learning" 2018, <https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1>, (01 June 2022)
- UCI Machine Learning Repository, “Gene Expression Cancer RNA-Seq Data Set”, 2016, <https://archive.ics.uci.edu/ml/datasets/gene+expression+cancer+RNA-Seq>, (24 May 2022)
- Eyup Kaan Ulgen, “Hierarchical Clustering”, 2021, <https://www.veribilimiokulu.com/hiyerarsik-kumeleme/>, (15 June 2022)
- Scikit Learn Official Web Site, “Agglomerative Clustering”,2022 , <https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html>, (10 June 2022)
- Prasad Pai, Hierarchical Clustering Explained, 2021, <https://towardsdatascience.com/hierarchical-clustering-explained-e59b13846da8>, (10 June 2022)
- Fahad Hussain, Umair Saeed, Ghulam Muhammad, Noman Islam and Ghazala Shafi Sheikh, “Classifying cancer patients based on DNA sequences using machine learning”, 2019
- Seyma Dogru, “Predicting Cancer Using Machine Learning on DNA Sequences”, 2022, <https://github.com/seymadogru/DNA-Dizileri-Uzerinde-Makine-Ogrenmesi-Kullanilarak-Kanser-Tahmininin-Yapilmasi.git>, (23 June 2022)
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | June 21, 2023 |
| Publication Date | June 21, 2023 |
| Published in Issue | Year 2023 |
Cite
The works published in Journal of Innovative Science and Engineering (JISE) are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.