Shreya Arya

Objective: This study aims to compare the predictive abilities of neural networks and Naive Bayes in forecasting drug–gene interactions of type 4 collagenase in gingival epithelialization. Materials and Methods: This study examines drug–gene interactions in type 4 collagen using a dataset encompassing drugs, genes, biochemical activity, mode of action, and molecular activity. Data normalization and handling of missing values are conducted to minimize the influence of larger variables. Machine learning algorithms such as neural networks and Naïve Bayes forecast or categorize drug–gene interactions. The neural network architecture, featuring 10 hidden layers, the ReLU activation function, the Adam optimizer, regularization, and a maximum of 100 iterations, is adept at solving complex problems. Results: Naive Bayes demonstrates a high area under the curve of 0.995 and notable classification accuracy (CA), but registers low overall accuracy and F1 score. It outperforms the Neural Network model in accuracy, precision, F1 score, and recall, but exhibits low specificity, potentially leading to elevated false‐positive rates. Conclusion: Predictions of drug–gene interactions for type 4 collagen hold promise for understanding biological pathways, identifying drug targets, designing targeted therapies, understanding disease mechanisms, and facilitating personalized medicine. The predictive models employed provide potential applications in personalized medicine, facilitating targeted therapies and disease management strategies. By elucidating biological pathways and drug targets, this research holds promise for advancing clinical interventions and improving patient outcomes in oral health care.

Designing microbiomes for applications in health, bioengineering, and sustainability is intrinsically linked to a fundamental theoretical understanding of the rules governing microbial community assembly. Microbial ecologists have used a range of mathematical models to understand, predict, and control microbiomes, ranging from mechanistic models, putting microbial populations and their interactions as the focus, to purely statistical approaches, searching for patterns in empirical and experimental data. We review the success and limitations of these modeling approaches when designing novel microbiomes, especially when guided by (inevitably) incomplete experimental data. Although successful at predicting generic patterns of community assembly, mechanistic and phenomenological models tend to fall short of the precision needed to design and implement specific functionality in a microbiome. We argue that to effectively design microbiomes with optimal functions in diverse environments, ecologists should combine data-driven techniques with mechanistic models---a middle, third way for using theory to inform design.