Department
Mathematics and Statistics
Major
Applied Mathematics
Research Advisor
Olbricht, Gayla R.
Samaranayake, V. A.
Thimgan, Matthew S.
Advisor's Department
Mathematics and Statistics
Second Advisor's Department
Biological Sciences
Funding Source
OURE Program; Ignition Grant Initiative
Abstract
In this research, a statistical model was developed to predict the lifespan of the fruit fly, Drosophila melanogaster, based on the sleep characteristics. Previously, a model was developed using variables based on the transition probabilities of a fly staying awake or asleep from minute-to-minute. This research builds on the previous work by incorporating additional variables based on traditional sleep metrics along with the transition probability variables into the modeling process. A method was first developed to automate the generation of the traditional sleep metrics, enabling them to be included in the model. Forward stepwise selection was used to determine an appropriate number of predictor variables before using best subset selection to determine the strongest model for that number of variables. Models were evaluated by comparing the original model with the model including the traditional variables.
Biography
Landon Oelschlaeger is a sophomore studying Applied Mathematics with a focus in Data Science and Statistics as well as Computer Science at the Missouri University of Science and Technology. He is a member of the Missouri University of Science and Technology Honors Academy. Landon is also a member of the Missouri University of Science and Technology Men's Track and Field Team where he throws javelin.
Research Category
Sciences
Presentation Type
Poster Presentation
Document Type
Poster
Location
Innovation Forum - 1st Floor Innovation Lab
Presentation Date
10 April 2024, 9:00 am - 12:00 pm
Statistical Modeling of Fruit Fly Based on Sleep Characteristics
Innovation Forum - 1st Floor Innovation Lab
In this research, a statistical model was developed to predict the lifespan of the fruit fly, Drosophila melanogaster, based on the sleep characteristics. Previously, a model was developed using variables based on the transition probabilities of a fly staying awake or asleep from minute-to-minute. This research builds on the previous work by incorporating additional variables based on traditional sleep metrics along with the transition probability variables into the modeling process. A method was first developed to automate the generation of the traditional sleep metrics, enabling them to be included in the model. Forward stepwise selection was used to determine an appropriate number of predictor variables before using best subset selection to determine the strongest model for that number of variables. Models were evaluated by comparing the original model with the model including the traditional variables.
