Browsing by Author "Mukesh Sunil Kumar"

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Prediction of hepatitis-C virus using statistical learning models
(BioMed Central Ltd, 2025) Shalini Kumari; Subhajit Das; Prashant Kumar Sonker; Agni Saroj; Mukesh Sunil Kumar
The hepatitis-c virus (HCV) is a viral infection that targets the liver and has emerged as a significant global health concern. This study investigates the classification of HCV patients by identifying the potential factors crucial for the progression and early detection of HCV. The study includes dataset of 615 HCV patients from the UCI Machine Learning Repository for illustrative purposes and analyzed it through machine learning models such as naive Bayes (NB), random forest (RF), support vector machine (SVM), logistic regression (LR), decision trees (DT), and artificial neural network (ANN). The models were evaluated using various performance metrics, and a comparative analysis using non-parametric tests was conducted to evaluate the statistical significance of the model. The empirical findings show that the RF model achieved the highest performance, with an accuracy of 96.71% with Brier score (BS) of 0.035 and Matthews correlation coefficient (MCC) of 0.849, an accuracy of 96.45% with BS of 0.031 and MCC of 0.837 and an accuracy 97.41% with BS of 0.026 and MCC of 0.947 when evaluated using all features, using selected features, and selected features with the application of the synthetic minority oversampling technique (SMOTE). The analytical methods have improved the overall predictive accuracy for HCV infection and will aid in the early identification of the disease. As a result, patients can be treated at the earliest possible stage, thereby increasing the number of lives saved. © The Author(s) 2025.
Profiling of human genes afflicted with nasopharyngeal carcinoma using microarray data
(Elsevier B.V., 2025) Rupam Raj; Subhashini; Kamalesh Kumar Kumar Patel; Mukesh Sunil Kumar
Background Nasopharyngeal carcinoma (NPC) is the most prevalent malignant carcinoma, and yet the biological mechanisms behind its pathogenesis are still unknown. Objective The objective of the research work was to apply bioinformatics tools to determine the essential expressed genes linked to NPC pathogenesis. Material and methods We retrieved three datasets (GSE12452, GSE13597, and GSE64634), from the Gene Expression Omnibus (GEO) portal. Differentially expressed genes (DEGs) determined between two groups called normal and NPC tissues. Gene ontology enrichment analysis (GO) performed through the online tool DAVID, and Kyoto Encyclopedia of Genes and Genomes (KEGG) online database used to identify pathways and progressions in which DEGs are highly involved in disease progression. Results We identified 77 commonly upregulated, 62 common downregulated in total 140 common DEGs in 3 datasets. The key cancer-causing pathways found in our study were mostly regulating cell adhesion molecules, Akt signalling pathway, cell cycle, cytochrome P450 and one carbon pool by folate. The interaction is shown between these DEGs through a protein protein interaction (PPI) network using STRING software and try to understand the effect these genes have on each other and noticed the most influential genes by studying their topological connectivity. The most influential genes, hub genes were identified by creating modules upon analysis of these modules. Conclusions We got 4 hub genes namely Aurora A (AURKA), Breast cancer susceptibility gene 1 (BRCA1), Fanconi anaemia group I protein (FANCI), and Abnormal spindle microtubule assembly (ASPM). For validation, we performed a survival analysis using GEPIA against the TCGA database, all four hub genes were upregulated in carcinoma cases compared to normal cases. These four biomarkers found can be used as potential therapeutic targets and as molecular signatures for early detection of NPC. © 2024
Stress-strength reliability estimation for non-identical strength: a study on power Muth distribution
(Springer, 2025) Prashant Kumar Sonker; Agni Saroj; Vikas Baranwal; Mukesh Sunil Kumar
This article explores the extension of the stress-strength reliability model of a system and the multi-component systems when the components of the system are considered to be non-identical. These components are separated into two categories. Each component of the system has some strength and the common random stress applied to it. The component strength of both the categories follows Power-Muth (PM) distribution and the stress applied to the components also follows PM distribution. It may follow any other lifetime distributions. Both the strength and the stress are independent of each other. The estimation of stress-strength reliability and multi-component stress-strength reliability is carried out using well-known ML and MPS estimation methods. Based on varying parameters, the reliability of the models is discussed. All the statistical calculations are done by using Monte Carlo simulation. Real data applicability of the extended model is also performed in the article. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2025.