Explore a comprehensive lecture on predicting the epigenome from DNA sequences presented by Hae Kyung Haky Im at the Computational Genomics Summer Institute (CGSI) 2023. Delve into the background of epigenome prediction, its importance in genomics, and the components of genome-wide association studies (GWAS). Examine gene-level association techniques and methods for predicting expression levels, while acknowledging the limitations of current approaches. Investigate deep learning methods applied to epigenome prediction, including data analysis, prediction models, and correlation studies. Learn about the Gene Pack 6 team's innovative ideas, such as TFprint and logistic regression for predicting epigenome features. Discover insights into transcription factor binding prediction and t-test results for identifying the best binding sites. Gain a thorough understanding of transcription factor scanning techniques and their applications in epigenome research.