Tried to use bioconductor and example code, not sure what went wrong but retrieving information from NCBI failed with earlier code and demonstration by AI and an outdated dplyr that isn't available yet for R couldn't get it up and running. So manual input by rank of string in the strands, and many of these multiple sclerosis gene fragments come from chromosome 2 non-coding region 2.12 but many don't as well. Corrected for the inverse relationship of the commercial line vs control as the fold change was input incorrectly but simple math corrected in and kept the field for an inverse comparison, all fold change values are in same direction of magnitude and these are the genes for our 41 top expressed genes found earlier to predict with 100% accuracy on samples alone and not foldchange values that a sample was healthy or had multiple sclerosis. We will use these genes as targets when building machine to predict a pathology of those analyzed thus far for Lyme disease, MS, mono, and fibromyalgia but also search for those EBV associated lymphomas and neck and throat sarcomas.

This lecture reviews the foundational biostatistical concepts covered in HSTA/HEPI 552. These concepts are essential for understanding the advanced statistical modeling techniques we'll explore in EPI 553.

Checklist confirming that you have completed all the technical setup required for EPI 553.

This document will verify that your R environment is properly configured for the course.

Homework 1 - STAT 512