21 research outputs found
Network topology similarities across cancer types: identifying central protein hubs for drug discovery
No full textA molecular-level understanding of cancer is essential for the development of effective therapies. Constructing protein-protein interaction (PPI) networks offers a valuable approach to identifying dysregulated driver genes and potential therapeutic targets. In this study, we modeled cancer PPI networks as metric spaces and applied mathematical and computational algorithms to analyze their structural and functional properties. Our findings reveal that these networks share a conserved architecture across different cancer types, with central zones enriched in essential proteins and critical regulatory pathways. Notably, zones 1 and 2 of the cancer PPI networks are uniquely enriched in specific pathways, underscoring their importance in the progression of cancer. These results highlight the potential of metric-based analysis of PPI networks to uncover key molecular targets and accelerate drug discovery in oncolog
Bifurcation of Exact Solutions for the Space-Fractional Stochastic Modified Benjamin–Bona–Mahony Equation
No full textThis paper studies the influence of space-fractional and multiplicative noise on the exact solutions of the space-fractional stochastic dispersive modified Benjamin–Bona–Mahony equation, driven in Ito’s sense by a multiplicative Wiener process. The bifurcation of the exact solutions is investigated, and novel fractional stochastic solutions are presented. The dependence of the solutions on the initial conditions is discussed. Due to the significance of the fractional stochastic modified Benjamin–Bona–Mahony equation in describing the propagation of surface long waves in nonlinear dispersive media, the derived solutions are significantly more helpful for and influential in comprehending diverse, crucial, and challenging physical phenomena. The effect of the Wiener process and the fractional order on the exact solutions are studied
Exploring the significance of zone 1 in human protein interaction networks: Identifying potential therapeutic targets for cancer treatment
No full textProtein-protein interaction networks are important tools for understanding the complex nature of biological processes and diseases, particularly cancer. However, the large size and complexity of these networks make it difficult to identify the most critical nodes that play important roles and act as critical regulators or mediators of biological processes in the networks. Identifying these key nodes within protein-protein interaction networks across different cancer types is crucial for elucidating the underlying molecular mechanisms and identifying potential therapeutic targets. Therefore, this study aims to analyse the significance of zone 1 in the human protein interaction network using the graph theory tool to identify the potential most important nodes across various cancer types
Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques
No full textBackground: Internet social media platforms have become quite popular, enabling a wide range of online users to stay in touch with their friends and relatives wherever they are at any time. This has led to a significant increase in virtual crime from the inception of these platforms to the present day. Users are harassed online when confidential information about them is stolen, or when another user posts insulting or offensive comments about them. This has posed a significant threat to online social media users, both mentally and psychologically. Methods: This research compares traditional classifiers and ensemble learning in classifying virtual harassment in online social media networks by using both models with four different datasets: seven machine learning algorithms (Nave Bayes NB, Decision Tree DT, K Nearest Neighbor KNN, Logistics Regression LR, Neural Network NN, Quadratic Discriminant Analysis QDA, and Support Vector Machine SVM) and four ensemble learning models (Ada Boosting, Gradient Boosting, Random Forest, and Max Voting). Finally, we compared our results using twelve evaluation metrics, namely: Accuracy, Precision, Recall, F1-measure, Specificity, Matthew’s Correlation Coefficient (MCC), Cohen’s Kappa Coefficient KAPPA, Area Under Curve (AUC), False Discovery Rate (FDR), False Negative Rate (FNR), False Positive Rate (FPR), and Negative Predictive Value (NPV) were used to show the validity of our algorithms. Results: At the end of the experiments, For Dataset 1, Logistics Regression had the highest accuracy of 0.6923 for machine learning algorithms, while Max Voting Ensemble had the highest accuracy of 0.7047. For dataset 2, K-Nearest Neighbor, Support Vector Machine, and Logistics Regression all had the same highest accuracy of 0.8769 in the machine learning algorithm, while Random Forest and Gradient Boosting Ensemble both had the highest accuracy of 0.8779. For dataset 3, the Support Vector Machine had the highest accuracy of 0.9243 for the machine learning algorithms, while the Random Forest ensemble had the highest accuracy of 0.9258. For dataset 4, the Support Vector Machine and Logistics Regression both had 0.8383, while the Max voting ensemble obtained an accuracy of 0.8280. A bar chart was used to represent our results, showing the minimum, maximum, and quartile ranges. Conclusions: Undoubtedly, this technique has assisted in no small measure in comparing the selected machine learning algorithms as well as the ensemble for detecting and exposing various forms of cyber harassment in cyberspace. Finally, the best and weakest algorithms were revealed
Modelling human protein interaction networks as metric spaces has potential in disease research and drug target discovery
No full textWe have recently shown by formally modelling human protein interaction networks (PINs) as metric
spaces and classified proteins into zones based on their distance from the topological centre that hub proteins are
primarily centrally located. We also showed that zones closest to the network centre are enriched for critically
important proteins and are also functionally very specialised for specific ‘house keeping’ functions. We proposed that
proteins closest to the network centre may present good therapeutic targets. Here, we present multiple pieces of
novel functional evidence that provides strong support for this hypothesis. We found that the human PINs has a highly connected signalling core, with the majority of proteins
involved in signalling located in the two zones closest to the topological centre. The majority of essential, disease
related, tumour suppressor, oncogenic and approved drug target proteins were found to be centrally located.
Similarly, the majority of proteins consistently expressed in 13 types of cancer are also predominantly located in zones
closest to the centre. Proteins from zones 1 and 2 were also found to comprise the majority of proteins in key KEGG
pathways such as MAPK-signalling, the cell cycle, apoptosis and also pathways in cancer, with very similar patterns
seen in pathways that lead to cancers such as melanoma and glioma, and non-neoplastic diseases such as measles,
inflammatory bowel disease and Alzheimer’s disease
Self-similarity of human protein interaction networks: a novel strategy of distinguishing proteins
No full textThe successful determination of reliable protein interaction networks (PINs) in several species in the
post-genomic era has hitherto facilitated the quest to understanding systems and structural properties of
such networks. It is envisaged that a clearer understanding of their intrinsic topological properties would
elucidate evolutionary and biological topography of organisms. This, in turn, may inform the
understanding of diseases’ aetiology. By analysing sub-networks that are induced in various layers identified
by zones defined as distance from central proteins, we show that zones of human PINs display self-similarity
patterns. What is observed at a global level is repeated at lower levels of inducement. Furthermore, it is
observed that these levels of strength point to refinement and specialisations in these layers. This may point
to the fact that various levels of representations in the self-similarity phenomenon offer a way of measuring
and distinguishing the importance of proteins in the network. To consolidate our findings, we have also
considered a gene co-expression network and a class of gene regulatory networks in the same framework. In
all cases, the phenomenon is significantly evident. In particular, the truly unbiased regulatory networks
show finer level of articulation of self-similarity.Web of Scienc
Strong simplicity of groups and vertex - transitive graphs
No full textMagister Scientiae - MScIn the course of exploring various symmetries of vertex-transitive graphs, we introduce the concept of quasi-normal subgroups in groups. This is done since the symmetries of vertex-transitive graphs are intimately linked to those, fait accompli, of groups. With this, we ask if the concept of strongly simple groups has a place for consideration.South Afric
Self-similarity of human protein interaction networks: a novel strategy of distinguishing proteins
No full textThe successful determination of reliable protein interaction networks (PINs) in several species in the
post-genomic era has hitherto facilitated the quest to understanding systems and structural properties of
such networks. It is envisaged that a clearer understanding of their intrinsic topological properties would
elucidate evolutionary and biological topography of organisms. This, in turn, may inform the
understanding of diseases’ aetiology. By analysing sub-networks that are induced in various layers identified
by zones defined as distance from central proteins, we show that zones of human PINs display self-similarity
patterns. What is observed at a global level is repeated at lower levels of inducement. Furthermore, it is
observed that these levels of strength point to refinement and specialisations in these layers. This may point
to the fact that various levels of representations in the self-similarity phenomenon offer a way of measuring
and distinguishing the importance of proteins in the network. To consolidate our findings, we have also
considered a gene co-expression network and a class of gene regulatory networks in the same framework. In
all cases, the phenomenon is significantly evident. In particular, the truly unbiased regulatory networks
show finer level of articulation of self-similarity
Extraction of Exact Solutions of Higher Order Sasa-Satsuma Equation in the Sense of Beta Derivative
No full textNearly every area of mathematics, natural, social, and engineering now includes research into finding exact answers to nonlinear fractional differential equations (NFDES). In order to discover the exact solutions to the higher order Sasa-Satsuma equation in the sense of the beta derivative, the paper will discuss the modified simple equation (MSE) and exponential rational function (ERF) approaches. In general, symmetry and travelling wave solutions of the Sasa-Satsuma equation have a common correlation with each other, thus we reduce equations from wave transformations to ordinary differential equations with the help of Lie symmetries. Actually, we can say that wave moves are symmetrical. The considered procedures are effective, accurate, simple, and straightforward to compute. In order to highlight the physical characteristics of the solutions, we also provide 2D and 3D plots of the results
Protein interaction networks as metric spaces: a novel perspective on distribution of hubs
No full textIn the post-genomic era, a central and overarching question in the analysis of protein-protein
interaction networks continues to be whether biological characteristics and functions of proteins such as lethality,
physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network
as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs,
highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein
interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned
and, from a distance point of view, centrally located
