Bioinformatics Methods in Biological Sciences: Computational Tools for Data Analysis
Keywords:
Bioinformatics, Machine Learning, Genomic Data Analysis, Convolutional Neural Networks, Gene Expression ClassificationAbstract
This research focuses on exploring the application of advanced bioinformatics methods and computational tools for data analysis in biological sciences. As biological data continue to grow at an exponential rate with genomics, proteomics, and transcriptomics, data analysis tools that are very efficient are crucial for accurate interpretation and discovery. Four key algorithms—that is, Support Vector Machine (SVM), Random Forest, K-Means Clustering and Convolutional Neural Networks (CNN)—were implemented and evaluated for their respective performance in analyzing high throughput biological datasets. Using secondary databases of genomics, the study used each algorithm and applied every algorithm against gene expression profiles and genetic patterns in order to classify and cluster. It is shown result with CNN achieve highest accuracy 94.6%, then Random Forest 91.2%, SVM 88.7% and K means 84.5%. Similar to the precision, recall, and F1 scores, the deep learning model had a better prediction performance. Our methods were also validated by experimental comparison with existing literature, where they were found to be up to 6% more accurate than previous models. It also shows the importance of computational tools in facilitating biological discovery and assist in data driven decision making in modern life sciences.
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