Methodological Advances in Molecular Biology: Techniques, Applications, and Challenges

Abstract

This research examines the modern methodological developments in molecular biology as combined with computation and machine learning algorithms to increase interpretation of the biological data and diagnostic. The algorithm of four repute, namely Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and k-Nearest Neighbors (k-NN) were used to evaluate the performance of their application to molecular datasets towards tasks such as gene expression classification, protein structure prediction and molecular pattern recognition. In the case of testing each algorithm’s efficiency, the study used synthetic and real-world datasets. Experiments showed that the CNN model produced a better result than other models with an accuracy of 94.6%, followed by RF’s accuracy of 91.2% as well as SVM and k-NN with 89.5% and 87.3% respectively. CNN also exhibited the highest sensitivity and specificity ate the levels of 95.1% and 93.2% respectively, which is why it is highly-suitable for image-based molecular applications. Two comparison tables and five measures of performance evaluation tables were utilized in presenting and analyzing results over algorithms. In addition, the literature review on recent studies supported the applicability of the combination of AI and molecular methods to overcome old-fashioned difficulties of scalability, accuracy and data complexity. The results indicate a great potential for introducing advanced algorithms in the molecular biology transmission channels with a view to improve the efficiency of research, diagnostics, and the development of therapeutic regimes