The Implementation of Regularized Markov Clustering with Pigeon Inspired Optimization Algorithm in Analyzing the SARS-CoV-2 (COVID-19) Protein Interaction Network

Proteins interact with other proteins, DNA, and other molecules, forming large-scale protein interaction networks and for easy analysis, clustering methods are needed. Regularized Markov clustering algorithm is an improvement of MCL where operations on expansion are replaced by new operations that update the flow distributions of each node. But to reduce the weaknesses of the RMCL optimization, Pigeon Inspired Optimization Algorithm (PIO) is used to replace the inflation parameters. The simulation results of IPC SARS-Cov-2 (COVID-19) inflation parameters  get the result of 42 proteins as the center of the cluster and 8 protein pairs interacting with each other. Proteins of COVID-19 that interact with 20 or more proteins are ORF8, NSP13, NSP7, M, N, ORF9C, NSP8, and NSP1. Their interactions might be used as a target for drug research

An enhanced binary bat and Markov clustering algorithms to improve event detection for heterogeneous news text documents

Event Detection (ED) works on identifying events from various types of data. Building an ED model for news text documents greatly helps decision-makers in various disciplines in improving their strategies. However, identifying and summarizing events from such data is a non-trivial task due to the large volume of published heterogeneous news text documents. Such documents create a high-dimensional feature space that influences the overall performance of the baseline methods in ED model. To address such a problem, this research presents an enhanced ED model that includes improved methods for the crucial phases of the ED model such as Feature Selection (FS), ED, and summarization. This work focuses on the FS problem by automatically detecting events through a novel wrapper FS method based on Adapted Binary Bat Algorithm (ABBA) and Adapted Markov Clustering Algorithm (AMCL), termed ABBA-AMCL. These adaptive techniques were developed to overcome the premature convergence in BBA and fast convergence rate in MCL. Furthermore, this study proposes four summarizing methods to generate informative summaries. The enhanced ED model was tested on 10 benchmark datasets and 2 Facebook news datasets. The effectiveness of ABBA-AMCL was compared to 8 FS methods based on meta-heuristic algorithms and 6 graph-based ED methods. The empirical and statistical results proved that ABBAAMCL surpassed other methods on most datasets. The key representative features demonstrated that ABBA-AMCL method successfully detects real-world events from Facebook news datasets with 0.96 Precision and 1 Recall for dataset 11, while for dataset 12, the Precision is 1 and Recall is 0.76. To conclude, the novel ABBA-AMCL presented in this research has successfully bridged the research gap and resolved the curse of high dimensionality feature space for heterogeneous news text documents. Hence, the enhanced ED model can organize news documents into distinct events and provide policymakers with valuable information for decision making

Dynamical Basis for Drug Resistance of HIV-1 Protease

<p>Abstract</p> <p>Background</p> <p>Protease inhibitors designed to bind to protease have become major anti-AIDS drugs. Unfortunately, the emergence of viral mutations severely limits the long-term efficiency of the inhibitors. The resistance mechanism of these diversely located mutations remains unclear.</p> <p>Results</p> <p>Here I use an elastic network model to probe the connection between the global dynamics of HIV-1 protease and the structural distribution of drug-resistance mutations. The models for study are the crystal structures of unbounded and bound (with the substrate and nine FDA approved inhibitors) forms of HIV-1 protease. Coarse-grained modeling uncovers two groups that couple either with the active site or the flap. These two groups constitute a majority of the drug-resistance residues. In addition, the significance of residues is found to be correlated with their dynamical changes in binding and the results agree well with the complete mutagenesis experiment of HIV-1 protease.</p> <p>Conclusions</p> <p>The dynamic study of HIV-1 protease elucidates the functional importance of common drug-resistance mutations and suggests a unifying mechanism for drug-resistance residues based on their dynamical properties. The results support the robustness of the elastic network model as a potential predictive tool for drug resistance.</p

Swarm Intelligent in Bio-Inspired Perspective: A Summary

This paper summarizes the research performed in the field of swarm intelligent in recent years. The classification of swarm intelligence based on behavior is introduced.&nbsp; The principles of each behaviors, i.e. foraging, aggregating, gathering, preying, echolocation, growth, mating, clustering, climbing, brooding, herding, and jumping are described. 3 algorithms commonly used in swarm intelligent are discussed.&nbsp; At the end of summary, the applications of the SI algorithms are presented

Providing SSPCO Algorithm to Construct Static Protein-Protein Interaction (PPI) Networks

Protein-Protein Inter-action Networks are dynamic in reality; i.e. Inter-actions among different proteins may be ineffective in different circumstances and times. One of the most crucial parameters in the conversion of a static network into a temporal graph is the well-tuning of transformation threshold. In this part of the article, using additional data, like gene expression data in different times and circumstances and well-known protein complexes, it is tried to determine an appropriate threshold. To accomplish this task, we transform the problem into an optimization one and then we solve it using a meta-heuristic algorithm, named Particle Swarm Optimization (SSPCO). One of the most important parts in our work is the determination of interestingness function in the SSPCO. It is defined as a function of standard complexes and gene co-expression data. After producing a threshold per each gene, in the following section we will discuss how using these thresholds, active proteins are determined and then temporal graph is created. For final assessment of the produced graph quality, we use graph clustering algorithms and protein complexes determination algorithms. For accomplishing this task, we use MCL, Cluster One, MCODE algorithms. Due to high number of the obtained clusters, the obtained results, if they have some special conditions, will filter out or be merged with each other. Standard performance criteria like Recal, Precision, and F-measure are employed. There is a new proposed criterion named Smoothness. Our experimental results show that the graphs produced by the proposed method outperform the previous methods

Swarm Intelligent in Bio-Inspired Perspective: A Summary

This paper summarizes the research performed in the field of swarm intelligent in recent years. The classification of swarm intelligence based on behavior is introduced. The principles of each behaviors, i.e. foraging, aggregating, gathering, preying, echolocation, growth, mating, clustering, climbing, brooding, herding, and jumping are described. 3 algorithms commonly used in swarm intelligent are discussed. At the end of summary, the applications of the SI algorithms are presented

Identifying Essential Proteins in Dynamic PPI Network with Improved FOA

Identification of essential proteins plays an important role for understanding the cellular life activity and development in postgenomic era. Identification of essential proteins from the protein-protein interaction (PPI) networks has become a hot topic in recent years. In this work, fruit fly optimization algorithm (FOA) is extended for identifying essential proteins, the extended algorithm is called EPFOA, which merges FOA with topological properties and biological information for essential proteins identification. The algorithm EPFOA has the advantage of identifying multiple essential proteins simultaneously rather than completely relying on ranking score identification individually. The performance of EPFOA is analyzed on dynamic PPI networks, which are constructed by combining the gene expression data. The experimental results demonstrate that EPFOA is more efficient in detecting essential proteins than the state-of-the-art essential proteins detection methods

Human Cytomegalovirus Reprograms the Expression of Host Micro-RNAs whose Target Networks are Required for Viral Replication: A Dissertation

The parasitic nature of viruses requires that they adapt to their host environment in order to persist. Herpesviruses are among the largest and most genetically complex human viruses and they have evolved mechanisms that manipulate a variety of cellular pathways and processes required to replicate and persist within their hosts. Human cytomegalovirus (HCMV), a member of the β- herpesvirus sub-family, has the capacity to influence the expression of many host genes in an effort to create an optimal environment for infection. One mechanism utilized by HCMV to alter gene expression is the host RNA interference (RNAi) pathway. This is evidenced by a requirement of host factors to process viral micro-RNAs (miRNAs) and by the dynamic expression of host miRNAs during infection. The work presented in this dissertation demonstrates that productive HCMV infection reprograms host miRNA expression in order to positively influence infection. I was able to identify a cohort of infection-associated host miRNAs whose change in expression during infection was highly significant. Using the enhancer-promoter sequences of this panel of host miRNAs, I statistically enriched for the presence of functional transcription factor binding sites that regulated the expression of two highly conserved clusters of host miRNAs: miR132/212 and miR143/145. Given that inhibiting their infection-associated change in expression during infection was detrimental to viral replication, it suggests that HCMV mechanistically influences the expression of these miRNA clusters. In order to determine the functional relevance of these miRNAs, I assembled a cohort of potential miRNA target genes using gene expression profiles from primary fibroblasts. By statistically enriching for miRNA recognition elements (MRE) in the respective 3’-UTR sequences, I generated a miRNA target network that includes thousands of host genes. I evaluated the efficacy of our novel miRNA target prediction algorithm by confirming the functionality of enriched MREs present in the 3’-UTR of KRas and by confirming anecdotal miRNA targets from published studies. Gene ontology terms enriched from infection-associated host miRNA target networks suggest that the utility of host miRNAs may extend to multiple host pathways that are required for viral replication. The targeting of multiple miRNAs to shared genes increased the statistical likelihood of target site enrichment. I propose that identifying cooperative miRNA networks is essential to establishing the functional relevance of miRNAs in any context. By combining contextual data on the relative miRNA/mRNA abundance with statistical MRE enrichments, one will be able to more accurately characterize the biological role of miRNAs