Hi, my name is Snelli Patel and I am a research associate under the Young Scientist Program with NASA Ames Research Center and Blue Marble Space Institute of Science. Today I'll be talking a little bit about my project, Machine Learning for the characterization of the effects of space radiation. To go into a little bit of background, astronauts that traveled deep into space receive long exposures to the space environment. And this environment is a composed of galactic cosmic rays and high energy charge particles which can cause DNA damage and carcinogenesis. This project combines the strength of genomic data with the response data from organismal samples that have been irradiated to investigate the genomic associations in radio sensitivity. In this project we explored the genomic associations in two data sets, one of which investigated the genetic susceptibility for radiation induced tumors and mice, and another one found on gene lab which investigated the radio sensitivity from blood immune cells and humans. The project I worked on leveraged a causal inference machine learning algorithm called CRISP. This algorithm, designed by the Frontier Development Lab astronaut health team, is a method used to explore causal features in high-dimensional omics data. The project aim was to investigate the potential causal genes that make someone sensitive to radiation. The project was composed of two parts, first the machine learning readiness pipelines and secondly the analyses from the machine learning. Below shows the workflow of this project which consisted of data acquisition, data preprocessing, machine learning implementation, and biological interpretation and analysis. We examined the outputted potentially causal features and mapped these variant locations to genes. Our results opened the door to more research into the putatively causal genes of radiation induced tumors and mice and radio sensitivity in humans. Thank you for listening.