Comparing Regularized Logistic Regression and Stochastic Gradient Descent in Predicting Drug-Gene Interactions of Inhibitors of Apoptosis Proteins in Periodontitis.

OBJECTIVE

Periodontitis, characterized by inflammation linked to apoptosis dysregulation, underscores the role of inhibitors of apoptosis proteins (IAPs) like survivin and cIAP1, implicated in disease progression and treatment resistance across various conditions. Our study aims to analyze the prediction of drug-gene interactions by machine learning techniques, combining regularized logistic regression and stochastic gradient descent (SGD) for efficient classification.

METHODS

Data from Probes-Drugs.org on IAP-based drug-protein interactions underwent rigorous annotation and outlier removal. A data robot tool trained machine learning models, regularized logistic regression and SGD (https://app.datarobot.com/new). Network analysis employed Cytoscape to construct and analyze the IAP network, identifying key hub nodes crucial in periodontitis pathogenesis.

RESULTS

The constructed IAP network comprised 376 nodes and 556 edges, revealing intricate drug-gene interactions with an average of 2957 neighbors per node. Ten hub nodes were identified as pivotal in regulating biological processes specific to periodontitis, suggesting their potential as therapeutic targets and biomarkers. Predictive models demonstrated high accuracy, with gradient descent achieving 93% and regularized logistic regression achieving 92% in identifying drug-gene interactions within the IAP network.

CONCLUSIONS

These findings highlight the utility of computational methods in elucidating molecular mechanisms underlying periodontitis, offering insights into potential therapeutic strategies targeting IAP-related pathways. Future research should focus on validating hub genes experimentally and integrating multi-omics data to advance precision medicine approaches in periodontitis treatment.