Classification of anti-oxidant proteins using novel physiochemical and conjoint-quad (PCQ) feature composition
The anti-oxidant proteins have a closer relation to disease control. Hence an accurate classification of antioxidant proteins by automated analysis is an essential process for the expansion of drugs for various diseases. Wet-lab experimental approaches are generally expensive and inefficient for the...
Gespeichert in:
Veröffentlicht in: | Multimedia tools and applications 2024-05, Vol.83 (16), p.48831-48857 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | The anti-oxidant proteins have a closer relation to disease control. Hence an accurate classification of antioxidant proteins by automated analysis is an essential process for the expansion of drugs for various diseases. Wet-lab experimental approaches are generally expensive and inefficient for the identification of anti-oxidant proteins. Novel methodologies like Physiochemical and Conjoint-Quad (PCQ) feature composition using the physio-chemical features combined with moment-based features is proposed in this work for the accurate classification of anti-oxidant proteins. In this proposed work, four techniques namely, proposed PCQ, k-spaced Amino Acid Pairs (CKSAAP), g-gap, and N-gram (N = 3) were applied to create different hybrid features from the anti-oxidant proteins efficiently. The Pearson Kernel-based Supervised Principal Component Analysis (PKSPCA) is proposed for the dimension reduction of the features and effective classification. To evaluate the proposed technique, ten-fold cross-validation and independent test datasets were utilized. On the testing data, the proposed method attained the best performance when compared with the previous techniques. This proposed method achieves 99% accuracy, sensitivity of 98% and specificity of 91% during the classification of the anti-oxidant proteins. |
---|---|
ISSN: | 1573-7721 1380-7501 1573-7721 |
DOI: | 10.1007/s11042-023-17498-w |