OPTIMIZATION OF FEATURE EXTRACTION FOR THE PREDICTION OF MACROMOLECULAR INTERACTIONS : OTE-24 APPROACH

In the field of molecular biology, where every interaction between macromolecules is of crucial importance, analyzing the structural features of biological macromolecules remains a major challenge. Traditional feature extraction techniques from protein sequences often prove to be inefficient. The reliability of the extracted information is sometimes questionable due to the complexity and volume of the data involved. The volume and complexity of this biological data compel researchers in the field to turn to computational feature extraction techniques. Over the years, several computational methods have been proposed to accurately extract relevant and representative information from macromolecule sequences within these large datasets. However, these extraction techniques are sometimes impractical, and the relevance of the extracted information may be limited. In this study, we propose a large-scale feature extraction method based on the correlation analysis of two physicochemical properties of amino acids: hydrophobicity and hydrophilicity, as well as the correlation between amino acids. The results of this research, evaluated using databases commonly utilized in previous studies, show an accuracy improvement of over 2.58% compared to existing methods.