Classification of Cyclin Proteins Using Amino Acid Composition and an SVM Approach: An In-Depth Analysis

Cyclins, commonly referred to as co-enzymes, are a pivotal family of proteins that modulate cellular growth by activating cell-cycle mediators, proving essential for the cell cycle. Due to the marked dissimilarity in their sequences, effective differentiation among cyclins remains a challenging endeavour. In this study, an innovative methodology was proposed, wherein the amino acid composition was utilized to inform an SVM-based classification approach. SVMs, being supervised machine learning algorithms, are typically employed for classification and regression tasks. From the data analyzed, eighteen (18) feature labels were extracted, culminating in an extensive set of thirteen thousand one hundred and fifty-one (13,151) discernible features. Employing the jackknife cross-validation technique revealed that this SVM-informed approach facilitated the identification of cyclins with an accuracy rate of 91.9%, a notable improvement from prior studies. Such advancements underscore the potential for more accurate and efficient differentiation of cyclins in future endeavours.