Classification of Gaussian trajectories with missing data in Boolean gene regulatory networks

Abstract

This paper studies the classification of gene regulatory networks (GRNs) modeled by probabilistic Boolean networks (PBNs). After observing Gaussian expression values of n genes at m consecutive time points, with consideration of missing data, an algorithm based on expectation maximization (EM) is proposed to estimate the parameters and infer the unknown parts of the networks in the maximum likelihood (ML) sense. Then the estimated values are plugged in to the Bayes classifier, which is optimal, and the performance of the classifier is investigated through various simulations.

Alireza Karbalayghareh

Senior Research Scientist, AI/ML

My research focuses on developing universal AI models of gene regulation in immune cells, capturing cis-regulation, trans-regulation, and intercellular signaling by using single-cell multiomic observational, spatial, time-resolved, and perturbational data across diverse biological contexts to understand, engineer, and reprogram the immune system to prevent and treat diseases.