COMPUTATIONAL APPROACHES IN BIOTECHNOLOGY FOR APPLIED BIOLOGICAL AND LIFE SCIENCE APPLICATIONS

Abstract

The rapid expansion of biological data generated through biotechnological advancements has necessitated the integration of computational approaches for effective analysis and interpretation. This study explored the application of computational techniques in biotechnology for applied biological and life science applications, with a specific focus on leukemia classification using gene-expression data. A publicly available microarray dataset comprising gene-expression profiles of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) was utilized. The dataset was preprocessed through transformation, normalization, and feature selection to address high dimensionality and improve data quality. Dimensionality reduction techniques were further applied to enhance computational efficiency. Multiple classification models, including Logistic Regression, Support Vector Machine, Random Forest, and K-Nearest Neighbors, were developed and evaluated using an independent test dataset. The results demonstrated that advanced computational models, particularly Support Vector Machine and Random Forest, achieved superior classification performance with high accuracy and robustness. The study highlighted the importance of computational approaches in extracting meaningful biological insights and improving decision-making in biomedical applications. Furthermore, the findings emphasized the role of computational biotechnology in bridging the gap between biological data generation and practical implementation, thereby contributing to advancements in healthcare and life sciences.