Epigenetic Pacemaker

Epigenetic Pacemaker models epigenetic state dynamics using conditional expectation maximization to estimate individual epigenetic trajectories and account for non-linear changes over time.

Key Features:

• Conditional Expectation Maximization Algorithm: Employs a statistical conditional expectation maximization framework to estimate epigenetic landscapes.
• Python Implementation: Implemented in Python with stated compatibility for version 3.6 and above.
• Non-linear Rate Modeling: Accounts for varying rates of epigenetic change over time to model non-linear progression of epigenetic modifications.

Scientific Applications:

• Estimation of Epigenetic Landscapes: Provides quantitative estimates of individual epigenetic states and trajectories across the lifespan.
• Study of Non-linear Epigenetic Aging: Enables investigation of how non-linear epigenetic changes correlate with aging and age-related disease processes.

Methodology:

Adapts the Pacemaker model of evolution to epigenetics and applies a conditional expectation maximization approach to estimate individual epigenetic states by modeling varying rates of change over time.