Enhancing Food Engineering Through Machine Learning in Analyzing Biomolecular Structures

In this paper, a detailed investigation has been outlined of protein sequence data with the help of a machine learning model for the identification of macromolecule types. The dataset was preprocessed from the Protein Data Bank and included missing values and duplicate structures, and the nominal variables have been encoded. The approach that was used in our study entailed the employment of Random Forest Classifier to forecast macromolecule types using the sequence data. Researchers divided the data into the training and testing data sets, thereafter, training the classifier, they tested the classifier's accuracy, precision, recall, and F1-score metrics. It can be stated that the relevance of this study is based on the necessity to discover the structural and functional properties of proteins in the field of bioinformatics. Hence, through an accurate classification of the macromolecule types, this approach assists in defining and describing the proteins, thus demystifying the roles that they play within biological cells. Our contribution also comprises the derivation of a highly reliable classification model that would be useful in identifying the type of macromolecules present from protein sequences. The model thus made high accurate determination of the different macromolecules and provided a clear framework which showed the model's efficacy in the differentiation of different macromolecules. Also, we describe the results of the analysis of macromolecule types of distribution and reveal the most often used categories within the set. This paper, therefore, demonstrates the potential of ML in bioinformatics and improves knowledge in predicting protein structure and function which may be of value in numerous biomedicine sectors.