Please use this identifier to cite or link to this item: https://zone.biblio.laurentian.ca/handle/10219/3742
Title: Predicting head and neck cancer in patients using epigenomics data and advanced machine learning methods
Authors: Oza, Virali
Keywords: Epigenomics;DNA methylation;histone;human genome;RNA;feature selection;classifiers;cluster analysis
Issue Date: 28-Jul-2021
Abstract: Epigenomics is the field of biology dealing with modifications of the phenotype that do not cause any alteration in the sequence of cell DNA. Epigenomics is formed up of the words epi and genomics, with epi deriving from the Greek prefix. The epi- in epigenomics refers to features that are "on top of" or "in addition to" the traditional genetic base for inheritance. As a result, it basically adds something on the top of DNA to modify its characteristics, thereby prohibiting some DNA behaviors. Such modifications occur in cancer cells and are the sole cause of cancer. Head and Neck is one of the most important parts of a human body. HNSC (head and neck squamous carcinoma) is one of the leading causes of cancer death, accounting for more than 650,000 cases and 330,000 deaths yearly throughout the world. Males, with a proportion ranging from 2:1 to 4:1, are slightly more affected than females. Four different types of data are used in this research to predict cancerous cells in the HNSCC patients namely methylation, histone, human genome and RNASequences. Nine feature selection methods and ten classifiers were used in this study. All data are obtained through open-source technologies in R. The data is processed to produce features, and the fine-tuned model is used to forecast Head and Neck cancer using statistical analysis and advanced machine learning techniques. Also, with the help of cluster analysis and Variable Importance measure we were able to find top 50 features which are important in the prediction of cancerous cells in HNSCC.
URI: https://zone.biblio.laurentian.ca/handle/10219/3742
Appears in Collections:Computational Sciences - Master's theses

Files in This Item:
File Description SizeFormat 
Thesis FINAL - Virali Oza - 31-Jul-21.pdf8.49 MBAdobe PDFView/Open


Items in LU|ZONE|UL are protected by copyright, with all rights reserved, unless otherwise indicated.