Inference of regulatory networks through temporally sparse data

Abstract

A major goal in genomics is to properly capture the complex dynamical behaviors of gene regulatory networks (GRNs). This includes inferring the complex interactions between genes, which can be used for a wide range of genomics analyses, including diagnosis or prognosis of diseases and finding effective treatments for chronic diseases such as cancer. Boolean networks have emerged as a successful class of models for capturing the behavior of GRNs. In most practical settings, inference of GRNs should be achieved through limited and temporally sparse genomics data. A large number of genes in GRNs leads to a large possible topology candidate space, which often cannot be exhaustively searched due to the limitation in computational resources. This paper develops a scalable and efficient topology inference for GRNs using Bayesian optimization and kernel-based methods. Rather than an exhaustive search over possible topologies, the proposed method constructs a Gaussian Process (GP) with a topology-inspired kernel function to account for correlation in the likelihood function. Then, using the posterior distribution of the GP model, the Bayesian optimization efficiently searches for the topology with the highest likelihood value by optimally balancing between exploration and exploitation. The performance of the proposed method is demonstrated through comprehensive numerical experiments using a well-known mammalian cell-cycle network.

Bayesian reinforcement learning for navigation planning in unknown environments

“…Several techniques for achieving autonomy in rescue operations have been developed in recent years, with reinforcement learning (RL) being one of the prominent methods. In recent years, RL techniques have achieved remarkable success across various domains, including network security, biological applications, and robotics (Alali and Imani, 2023 , 2024 ; Elguea-Aguinaco et al, 2023 ; Ravari et al, 2023 ; Alali et al, 2024 ; Asadi et al, 2024 ). For autonomy in rescue operations, various RL techniques have been developed for single-agent and multi-agent settings (Imanberdiyev et al, 2016 ; Zhang et al, 2018 ; Bøhn et al, 2019 ; Lin et al, 2019 ; Niroui et al, 2019 ; Sampedro et al, 2019 ; Ebrahimi et al, 2020 ; Hu et al, 2020 ; Wu et al, 2021 ).…”