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Using Transcriptomic Data to Predict Biomarkers for Subtyping of Lung Cancer
University of Skövde, School of Bioscience.
2021 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Lung cancer is one the most dangerous types of all cancer. Several studies have explored the use of machine learning methods to predict and diagnose this cancer. This study explored the potential of decision tree (DT) and random forest (RF) classification models, in the context of a small transcriptome dataset for outcome prediction of different subtypes on lung cancer. In the study we compared the three subtypes; adenocarcinomas (AC), small cell lung cancer (SCLC) and squamous cell carcinomas (SCC) with normal lung tissue by applying the two machine learning methods from caret R package. The DT and RF model and their validation showed different results for each subtype of the lung cancer data. The DT found more features and validated them with better metrics. Analysis of the biological relevance was focused on the identified features for each of the subtypes AC, SCLC and SCC. The DT presented a detailed insight into the biological data which was essential by classifying it as a biomarker. The identified features from this research may serve as potential candidate genes which could be explored further to confirm their role in corresponding lung cancer types and contribute to targeted diagnostics of different subtypes. 

Place, publisher, year, edition, pages
2021. , p. 39
Keywords [en]
lung cancer, decision tree, random forest, accuracy, cross-validation, machine learning
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:his:diva-21598OAI: oai:DiVA.org:his-21598DiVA, id: diva2:1682705
Subject / course
Bioinformatics
Educational program
Bioinformatics - Master’s Programme
Supervisors
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Available from: 2022-07-11 Created: 2022-07-11 Last updated: 2022-07-15Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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  • nn-NB
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  • Other locale
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Output format
  • html
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  • asciidoc
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