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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-107092 |
| Date | January 2021 |
| Creators | Wassbjer, Mattias |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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