Predicting the Epigenome from DNA Sequence - Are We There Yet?

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.

Introduction
Background
Why Predict the Epigenome
Components of GWAS
Gene level association
Predicting expression levels
Limitations
Deep Learning Methods
Data
Prediction
Correlation
Gene Pack 6
Team
Idea
TFprint
Logistic Regression
Predicting Epigenome
Transcription Vector Binding
Ttest Results
Best site
Transcription Factor Scan
Summary