Gene expression analysis using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| Формат: | Дисертація |
| Мова: | English |
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Brac University
2021
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| Онлайн доступ: | http://hdl.handle.net/10361/15337 |
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10361-153372022-01-26T10:19:54Z Gene expression analysis using machine learning Mostafa, Nafis Ajwad, Rasif Department of Computer Science and Engineering, Brac University Gene expression PCA Autoencoder GeCuMiDa database Machine Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 17-18). Cancer is a multifactorial disorder that occurs due to the complex interaction between the environment and gene. The susceptibility of a person to cancer depends on his genetic build-up. Recently, the study of genomes in discovering the interaction between disease and genes and how their interaction leads to specific phenotype, has grown exponentially. To analyze the expression of thousands of genes, one of the most important and revolutionary techniques used in genomics and systems biology is high-throughput microarray technology. To produce an accurate prognosis from such high-dimensional gene expressional data, machine learning can be an ideal choice. In this paper, we have tried to apply principal component analysis (PCA) and autoencoder on a brain cancer gene expression data retrieved from CuMiDa database and make an analysis of which technique produce better and more accurate reduced dimensional vectors and how different classical machine learning algorithms performs on these newly generated datasets. Finally, we also discussed how to improve these current techniques and how it can lead to better and sophisticated outcomes. Nafis Mostafa B. Computer Science 2021-10-18T06:17:20Z 2021-10-18T06:17:20Z 2021 2021-01 Thesis ID 20241055 http://hdl.handle.net/10361/15337 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 18 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Gene expression PCA Autoencoder GeCuMiDa database Machine Learning |
| spellingShingle |
Gene expression PCA Autoencoder GeCuMiDa database Machine Learning Mostafa, Nafis Gene expression analysis using machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Ajwad, Rasif |
| author_facet |
Ajwad, Rasif Mostafa, Nafis |
| format |
Thesis |
| author |
Mostafa, Nafis |
| author_sort |
Mostafa, Nafis |
| title |
Gene expression analysis using machine learning |
| title_short |
Gene expression analysis using machine learning |
| title_full |
Gene expression analysis using machine learning |
| title_fullStr |
Gene expression analysis using machine learning |
| title_full_unstemmed |
Gene expression analysis using machine learning |
| title_sort |
gene expression analysis using machine learning |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15337 |
| work_keys_str_mv |
AT mostafanafis geneexpressionanalysisusingmachinelearning |
| _version_ |
1814308967278444544 |