Article in Press
Biclustering algorithms are effective techniques for uncovering previously unknown facts, hidden in gene expression data. The usefulness of clustering algorithms for analyzing gene expression data has been limited because they partition data into mutually exclusive groups rather than real, overlapping gene sets. These gene sets can reveal novel insights helpful for disease diagnosis, prognosis and drug development. This...
Article in Press
The adaptation of Mycobacterium Tuberculosis to varied environmental stresses is a fundamental aspect of its pathogenesis and survival. Universal Stress Proteins (USPs) have emerged as pivotal players in this adaptive response, with their expression triggered by an array of stressors. In this study, we propose a novel approach to predict USPs using a Support Vector Machine (SVM) model, aiming to enhance our comprehension...
Article in Press
This research harnesses the capabilities of explainable artificial intelligence (XAI) techniques, including Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, and Multi-Layer Perceptron, to advance maternal outcome predictions during pregnancy, with a specific focus on critical determinants such as age, pack cell volume, weight, and maternal blood pressure. Ethical clearance was obtained for comprehensive...
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