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Analysis of the relation between RNA and RBPs using machine learning
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
2021 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Analys av relationen mellan RNA och RBPs med hjälp av maskininlärning (Swedish)
Abstract [en]

The study of RNA-binding proteins has recently increased in importance due to discoveries of their larger role in cellular processes. One study currently conducted at Umeå University involves constructing a model that will be able to improve our knowledge about T-cells by explaining how these cells work in different diseases. But before this model can become a reality, Umeå Univerity needs to investigate the relation between RNA and RNA-binding proteins and find proteins of which highly contribute to the activity of the RNA-binding proteins. To do so, they have decided to use four penalized regression Machine Learning models to analyse protein sequences from CD4 cells. These models consist of a ridge penalized model, an elastic net model, a neural network model, and a Bayesian model. The results show that the models have a number of RNA-binding protein sequences in common which they list as highly decisive in their predictions.

Place, publisher, year, edition, pages
2021. , p. 38
Keywords [en]
Machine Learning, Supervised Learning, Linear Regression, RNA-binding Proteins, LIME, T-Cells, CD4 Cells, K-mer, Bag of Words, RBP Activity Prediction
National Category
Computer Sciences Cell Biology
Identifiers
URN: urn:nbn:se:lnu:diva-107092OAI: oai:DiVA.org:lnu-107092DiVA, id: diva2:1596786
External cooperation
Umeå universitet
Educational program
Computer Engineering Programme, 180 credits
Supervisors
Examiners
Available from: 2021-09-23 Created: 2021-09-23 Last updated: 2021-09-23Bibliographically approved

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fulltext(2883 kB)97 downloads
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Wassbjer, Mattias
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CiteExportLink to record
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Citation style
  • apa
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