Big Data in Plant Biotechnology: Leveraging Bioinformatics to Discover Novel Anticancer Agents from Flora

Abstract

The findings provide information regarding the distribution, activity values, and relationships of the different types of bioactive compounds regarding their structural diversity, bioactivity, and possible interactions. Our results in pie chart representing the percentage distribution of four major classes of compounds; terpenoids (37.5%), alkaloids (25%), flavonoids (20.8%), and phenolics (16.7%). From this chart, it can be inferred that terpenoids are the most predominant class in the dataset followed by alkaloids, which indicates either their high prevalence or significant contribution in the analyzed samples. The activity scores for compound types and highlights variability within each category. Flavonoids have a higher range with a higher median activity than terpenoids and phenolics, which have slightly lower activity scores on average; however, variances are greatest for phenolics with some extreme outlier values. This suggests their differential bioactivity profiles from compound type to compound type, most likely exhibiting higher active properties as flavonoids. And the correlation heatmap showing several crucial variables related to bioactivity activity score cell line activity toxicity score and prediction of activity. However, correlatively low values indicate that they do not directly relate in significance; thus, each figure may isolate an instance of showing interaction profiles for both activity and toxicity properties of compounds under investigation.