Computational Linguistic Analysis for the Isolation of Propitious miRNA Bio- indicators in Esophageal Cancer

Abstract Introduction MicroRNAs (miRNAs) serve as diminutive, non-coding RNA molecules that are instrumental in the ontogenesis of Esophageal Cancer. While isolated investigations elucidate invaluable facets of miRNA mechanisms in this malignancy, their circumscribed scope hampers a holistic comprehension of miRNAs' contributions to the pathophysiology and therapeutic strategy of Esophageal Cancer. Methods To mitigate the idiosyncratic biases inherent in isolated studies, we harnessed a text mining-based analytics to discern the roles of miRNAs in esophageal cancer and their viability as bio-indicators. Abstracts from scholarly articles were tokenized, and salient biomedical lexemes were extracted for thematic modeling. Five machine learning paradigms—Logistic Regression(LR), Naïve Bayes,DCA ,Random Forests, and Support Vector Machines (SVM)—were deployed for the categorization of Esophageal Cancer. Feature saliency was evaluated to architect miRNA-Esophageal Cancer interaction networks. Results Our scrutiny unearthed 5 topics within miRNA studies pertinent to Esophageal Cancer, revealing a topic-specific proclivity among miRNAs.Logistic Regression manifested an auspicious prognostic capability for Esophageal Cancer, boasting an accuracy metric exceeding 57%. Remarkably, miR-21 surfaced as a pivotal bio-indicator for Esophageal Cancer prognosis, targeting an array of genes and signaling cascades implicated in esophageal Cancer Conclusion This integrative methodology furnishes overarching insights into the miRNA-Esophageal Cancer nexus and substantiates the prospective utility of miRNAs as bio-indicators for this malignancy.