Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
COMPREHENDING THE EFFECT OF SMOKING IN LUNG CANCER BY USING RULE NETWORKS
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

With advances in computer science, computational analytics and more specifically machine learning algorithms are increasingly being used in many fields of science. These algorithms are highly valuable for the analysis of large and complex biological data that has to be performed to convey the information the raw data contains so that it can be further interpreted. Cancer is one of the most significant health problems of our day due to its huge burden on mortality, quality of life and health expenditures. The etiology and mechanism of many types of cancer are yet to be elucidated, therefore research on the subject is of paramount importance. Lung cancer especially is of great significance because of high incidence and rate of mortality, being the leading cause of cancer-related deaths. Although smoking is considered as the most prominent risk factor for lung cancer, its mechanism of action has not been figured out comprehensively yet. The aim of this project is using machine learning algorithms to generate rule-based models and create rule networks which can help better understand the effects of smoking on gene expression and metabolite levels that are potentially associated with lung cancer. For this task, we tried two feature selection algorithms (Boruta and Monte Carlo) and the ROSETTA software to generate rule-based classifier models. The resulting rule networks were schematically visualized using VisuNet.

Place, publisher, year, edition, pages
2019. , p. 26
Series
IT ; 19011
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-395658OAI: oai:DiVA.org:uu-395658DiVA, id: diva2:1364186
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-22Bibliographically approved

Open Access in DiVA

fulltext(1062 kB)4 downloads
File information
File name FULLTEXT01.pdfFile size 1062 kBChecksum SHA-512
9f3d5eb34791e99526c4aea8ff2f576ee01690af83a2c2a7a373d2dbeff3a1f19a22839299f34b19d810d8d3693c5df3318099ade5aecb85e5bcea58127eebcd
Type fulltextMimetype application/pdf

By organisation
Department of Information Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 4 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 16 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf