Optimized time-lag differential method for constructing gene regulatory network

An optimal Bayesian intervention policy in response to unknown dynamic cell stimuli

Interventions in gene regulatory networks (GRNs) aim to restore normal functions of cells experiencing abnormal behavior, such as uncontrolled cell proliferation. The dynamic, uncertain, and complex nature of cellular processes poses significant challenges in determining the best interventions. Most existing intervention methods assume that cells are unresponsive to therapies, resulting in stationary and deterministic intervention solutions. However, cells in unhealthy conditions can dynamically respond to therapies through internal stimuli, leading to the recurrence of undesirable conditions. This paper proposes a Bayesian intervention policy that adaptively responds to cell dynamic responses according to the latest available information. The GRNs are modeled using a Boolean network with perturbation (BNp), and the fight between the cell and intervention is modeled as a two-player zero-sum game. Assuming an incomplete knowledge of cell stimuli, a recursive approach is developed to keep track of the posterior distribution of cell responses. The proposed Bayesian intervention policy takes action according to the posterior distribution and a set of Nash equilibrium policies associated with all possible cell responses. Analytical results demonstrate the superiority of the proposed intervention policy against several existing intervention techniques. Meanwhile, the performance of the proposed policy is investigated through comprehensive numerical experiments using the p53-MDM2 negative feedback loop regulatory network and melanoma network. The results demonstrate the empirical convergence of the proposed policy to the optimal Nash equilibrium policy.

Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning

Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discriminative feature genes, and existing methods fail to deal with this problem efficiently. This article proposes a novel metaheuristic approach for gene feature extraction, called variable neighborhood learning Harris Hawks optimizer (VNLHHO). First, the F-score is used for a primary selection of the genes in gene expression data to narrow down the selection range of the feature genes. Subsequently, a variable neighborhood learning strategy is constructed to balance the global exploration and local exploitation of the Harris Hawks optimization. Finally, mutation operations are employed to increase the diversity of the population, so as to prevent the algorithm from falling into a local optimum. In addition, a novel activation function is used to convert the continuous solution of the VNLHHO into binary values, and a naive Bayesian classifier is utilized as a fitness function to select feature genes that can help classify biological tissues of binary and multi-class cancers. An experiment is conducted on gene expression profile data of eight types of tumors. The results show that the classification accuracy of the VNLHHO is greater than 96.128% for tumors in the colon, nervous system and lungs and 100% for the rest. We compare seven other algorithms and demonstrate the superiority of the VNLHHO in terms of the classification accuracy, fitness value and AUC value in feature selection for gene expression data.