Daris Francis, Kandaswami Kotteeswaran, Pramod Padinhare Veedu
Severity of Neuropathy-Related Disability and Associated Factors of Diabetic Peripheral Neuropathy in a Tertiary Healthcare Center: A Comparative Cross-Sectional Study
Cureus
analysis factors lead functionality transcriptional activation domains remains crucial yet challenging task owing significant diversity sequences intrinsically disordered nature almost existing methods aimed predict activation domains involved traditional machine learning approaches logistic regression unable capture complex patterns data plain convolutional neural networks limited exploration structural features however tremendous potential inspection structural properties activation domains opportunity investigate complex relationships features residues sequence address utilized power graph neural networks represent structural data form nodes edges allowing nodes exchange information among experimented two kinds graph formulations one involving residues nodes assigning atoms nodes logistic regression model also developed analyze feature importance models several feature combinations experimented residuelevel gnn model amino acid type residue position acidicbasicaromatic property secondary structure feature combination gave best performing model accuracy f1 score auroc 979 71 971 respectively outperformed existing methods literature applied dataset used among structurebased features analyzed amphipathic property helices also proved important feature classification logistic regression results showed dominant feature makes sequence functional frequency different types amino acids sequence results consistent shown functional sequences acidic aromatic residues whereas basic residues seen nonfunctional sequences