Detection of dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach

Heterogeneous network approaches to protein pathway prediction

Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.

Repurposing of Anti-Cancer Drugs Against Moderate and Severe COVID Infection: A Network-Based Systems Biological Approach

BACKGROUND

The COVID-19 pandemic caused by SARS-CoV-2 is an unparalleled health risk, needing fast antiviral medication development. One of the most effective strategies for developing therapies against novel and emerging viruses is drug repurposing. Recently, systems biology approaches toward the discovery of repurposing medications are gaining prominence.

AIM

This study aimed to implement a systems biology approach to identify crucial drug targets as well as potential drug candidates against COVID infection.

METHODS

Our approach utilizes differential gene expression in COVID conditions that enable the construction of a protein-protein interaction (PPI) network. Core clusters were extracted from this network, followed by molecular enrichment analysis. This process identified critical drug targets and potential drug candidates targeting various stages of COVID-19 infection.

RESULTS

The network was built using the top 200 differently expressed genes in mild, moderate, and severe COVID-19 infections. Top 3 clusters for each disease condition were identified, representing the core mechanism of the network. Molecular enrichment revealed the majority of the pathways in the mild state were associated with transcription regulation, protein folding, angiogenesis, and cytokine-signaling pathways. Whereas, the enriched pathways in moderate and severe disease states were predominately linked with the immune system and apoptotic processes, which include NF-kappaB signaling, cytokine signaling, TNF-mediated signaling, and oxidative stress-induced cell death. Further analysis identifies 28 potential drugs that can be repurposed to treat moderate and severe COVID-19, most of which are currently used in cancer treatment.

CONCLUSION

Interestingly, some of the proposed drugs have demonstrated inhibitory effects against SARS-CoV-2, as supported by literature evidence. Overall, the drug repurposing method described here will help develop potential antiviral medications to treat emerging COVID strains.

Gaussian Perturbation Specular Reflection Learning and Golden-Sine-Mechanism-Based Elephant Herding Optimization for Global Optimization Problems

Elephant herding optimization (EHO) has received widespread attention due to its few control parameters and simple operation but still suffers from slow convergence and low solution accuracy. In this paper, an improved algorithm to solve the above shortcomings, called Gaussian perturbation specular reflection learning and golden-sine-mechanism-based EHO (SRGS-EHO), is proposed. First, specular reflection learning is introduced into the algorithm to enhance the diversity and ergodicity of the initial population and improve the convergence speed. Meanwhile, Gaussian perturbation is used to further increase the diversity of the initial population. Second, the golden sine mechanism is introduced to improve the way of updating the position of the patriarch in each clan, which can make the best-positioned individual in each generation move toward the global optimum and enhance the global exploration and local exploitation ability of the algorithm. To evaluate the effectiveness of the proposed algorithm, tests are performed on 23 benchmark functions. In addition, Wilcoxon rank-sum tests and Friedman tests with 5% are invoked to compare it with other eight metaheuristic algorithms. In addition, sensitivity analysis to parameters and experiments of the different modifications are set up. To further validate the effectiveness of the enhanced algorithm, SRGS-EHO is also applied to solve two classic engineering problems with a constrained search space (pressure-vessel design problem and tension-/compression-string design problem). The results show that the algorithm can be applied to solve the problems encountered in real production.

Gaussian Perturbation Specular Reflection Learning and Golden-Sine-Mechanism-Based Elephant Herding Optimization for Global Optimization Problems

Elephant herding optimization (EHO) has received widespread attention due to its few control parameters and simple operation but still suffers from slow convergence and low solution accuracy. In this paper, an improved algorithm to solve the above shortcomings, called Gaussian perturbation specular reflection learning and golden-sine-mechanism-based EHO (SRGS-EHO), is proposed. First, specular reflection learning is introduced into the algorithm to enhance the diversity and ergodicity of the initial population and improve the convergence speed. Meanwhile, Gaussian perturbation is used to further increase the diversity of the initial population. Second, the golden sine mechanism is introduced to improve the way of updating the position of the patriarch in each clan, which can make the best-positioned individual in each generation move toward the global optimum and enhance the global exploration and local exploitation ability of the algorithm. To evaluate the effectiveness of the proposed algorithm, tests are performed on 23 benchmark functions. In addition, Wilcoxon rank-sum tests and Friedman tests with 5% are invoked to compare it with other eight metaheuristic algorithms. In addition, sensitivity analysis to parameters and experiments of the different modifications are set up. To further validate the effectiveness of the enhanced algorithm, SRGS-EHO is also applied to solve two classic engineering problems with a constrained search space (pressure-vessel design problem and tension-/compression-string design problem). The results show that the algorithm can be applied to solve the problems encountered in real production.

Elephant Herding Optimization: Variants, Hybrids, and Applications

Elephant herding optimization (EHO) is a nature-inspired metaheuristic optimization algorithm based on the herding behavior of elephants. EHO uses a clan operator to update the distance of the elephants in each clan with respect to the position of a matriarch elephant. The superiority of the EHO method to several state-of-the-art metaheuristic algorithms has been demonstrated for many benchmark problems and in various application areas. A comprehensive review for the EHO-based algorithms and their applications are presented in this paper. Various aspects of the EHO variants for continuous optimization, combinatorial optimization, constrained optimization, and multi-objective optimization are reviewed. Future directions for research in the area of EHO are further discussed.