Cancer Prediction based on DNA Next Generation Genomic Sequencing using Machine Learning Algorithms
| Author | Lateef, Marwa Assim Ayfan | |
|---|---|---|
| metadata.dc.contributor.department | UNIVERSITY OF BAHRAIN, College of Information Technology | |
| metadata.dc.date.accessioned | 2026-09-20T15:42:57Z | |
| Publication date | 2021 | |
| Degree level | Master | |
| Abstract | Abstract: Cancer is a dangerous disease that threatens human life and leads to severe side effects and consequences, and in many cases, it leads to death. Cancer occurs when the cells start to divide and grow exponentially and uncontrollably. The problem is that cancer is often not discovered early in its first stage until after a long period has passed. Hence, the treatment, in this case, is more complicated and sometimes is not possible. Different diseases, including cancer, progress because of the mutations in the Deoxyribonucleic Acid (DNA) genomic sequence. Genes’ mutations NextGeneration Sequencing (NGS) technology advancement enabled scientists and bioinformaticians to study genomic sequencing, which helps identify the effects of gene mutations on developing different types of diseases. This thesis focuses on predicting and classifying different cancers based on NGS technology by utilizing Ribonucleic Acid Sequence (RNA-Seq) gene expression data using various machine learning algorithms. This research aims to find the most potential biomarkers for various cancers that significantly help in personalized medicine. Four different approaches and models have been proposed in this study, namely Mutual Information-Support Vector Machine, Recursive Feature EliminationSupport Vector Machine, Random Forest. The fourth model is a novel ensemble approach that discovers the most relevant genes causing a specific type of cancer. Moreover, four datasets (COAD, LUAD, BRCA, and Combined dataset from the first three datasets) were applied and tested with the developed models, and the results were justified and validated. A comparison study of the obtained results with the existing research has been done. The experimental results within accuracy scores of up to 99.4% show that the proposed models compared to the state-of-the-art methods, efficiently predict and classify cancer and discover the most potential genes to develop a specific type of cancer. | |
| metadata.dc.identifier.other | https://digitalrepository.uob.edu.bh/id/680bd2f1-668f-4295-ab20-bb87df32006e | https://digitalrepository.uob.edu.bh/id/680bd2f1-668f-4295-ab20-bb87df32006e | |
| metadata.dc.identifier.other | Drupal node 1145 | |
| metadata.dc.identifier.other | https://digitalrepository.uob.edu.bh/en/dar/cancer-prediction-based-dna-next-generation-genomic-sequencing-using-machine-learning | |
| metadata.dc.identifier.uri | http://165.227.56.122/handle/123456789/6056 | |
| Language | en | |
| Link | https://digitalrepository.uob.edu.bh/en/_flysystem/fedora/2024-09/cancer-prediction-based-on-dna-next-generation-genomic-sequencing-using-machine-learning-algorithms.pdf | |
| Title | Cancer Prediction based on DNA Next Generation Genomic Sequencing using Machine Learning Algorithms | |
| Type | Master's Thesis |
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