Year: 2024
Type: conference-paper
Abstract: The inheritance of biological processes is a wellexplored topic in biology literature, yet the advancement of formal mathematical models and robust data analysis frameworks remains limited. We present the Nested Inheritance Dynamics Algorithm (NIDA), a Bayesian framework, an extension of the nested Dirichlet Process (nDP) into a multiscale framework. NIDA is designed to investigate the mechanisms governing biological inheritance, stability, and transformation across generations. It operates on two interconnected levels: the primary level models all processes occurring within an individual's lifespan, while the secondary level explores the persistence or evolution of these processes over time. This approach supports system modeling at various scales, integrates seamlessly with existing developmental and inheritance models, and offers a powerful tool for studying complex biological phenomena.
Author: Bahman Moraffah
Institution: Arizona State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409156742