Quantitative Methods in Biological Sciences: Tools for Data Analysis and Interpretation
Keywords:
Biological Data Analysis, Quantitative Methods, Machine Learning, Random Forest, Principal Component Analysis.Abstract
The complexity and quantity of biological data are expanding, and quantitative methods and computation tools are needed for analysis and interpretation. This research explores the utilization of four of the most popular algorithms – K-Means Clustering, Support Vector Machine (SVM), Random Forest and Principal Component Analysis (PCA) – to perform analysis on the biological datasets. Each of the algorithms was programmed and run on simulated multi-dimensional biological data to assess their ability to classify, their efficiency and in feature extraction. Experimental results found that Random Forest algorithm has the best classification accuracy of 95.6%, SVM has 92.3%, K-Means has 88.7% and PCA-based analysis has an overall interpretation accuracy of 85.4%. Comparative evaluation was also carried out based on precision, recall, F1-score, and time of processing to measure each methods effectiveness in real world biological applications. The study conforms that hybrid methods of dimensionality reduction and supervised learning provide better performance. These results are consistent with the related work (metaTP and ToxDAR) that confirms the increasing significance of automated workflows and statistical modeling in contemporary biology. On balance, this study shows that the combination of an algorithmic analysis with biological interpretation makes the quality of decision-making much stronger in such domains as genomics, proteomics, and medical diagnostics
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