Application of Big Biological Data Analysis Techniques in Molecular Biology

To enhance the storage and analysis efficiency of large-scale data in molecular biology, a distributed database model accommodating diverse data types was developed, complemented by machine learning optimization algorithms for molecular sequence alignment, structural prediction, and molecular interaction analysis. Results indicate that support vector machine and hidden Markov model achieved accuracy rates of 89.2% and 90.1%, respectively, with processing speeds surpassing 900 entries per second for protein sequence and interaction data, showing significant improvement over traditional methods. This analytical framework demonstrates potential for efficient data management and interaction identification among molecules within massive datasets.