Linear non-coding RNA (lncRNA) has recently attracted a variety of research, critical attention to some intriguing new knowledge about its essential functions. Thousands of lncRNAs were discovered in absurdly short durations because of tools like RNA-Seq. Only a handful of them should be intrinsically depicted, although, due to lesser citation incidence. Wet lab tests to elucidate the roles of lncRNA are complex, laborious, and sometimes ridiculously expensive. This research explores the critical topic of generating molecular docking toward determining the essential functions of lncRNAs. The model provided here uses a measure based on a systematic review known as AvgSim on a heterogeneous network infrastructure to predict various lncRNA (HIN) RNA activities. The same framework is designed with proposed statistics on the lncRNA enzyme or activity relationships and information on the lncRNA coexpression and linkage proteins. The proposed methodology forecasts probable capabilities for 2,695 lncRNAs with a reliability of 73.68% out of 2,758 lncRNAs evaluated for investigation and was performed remarkably better than other methodologies like a random forest for an impartial training dataset. These same linked roles of two well-known lncRNAs are investigated inside as an example. The findings were confirmed by research observations identified in this review.

Breast cancer remains one of the most prevalent and life-threatening malignancies among women worldwide, demanding timely detection and accurate prediction of tumor progression. Recent advances in artificial intelligence (AI) and machine learning (ML) have revolutionized breast cancer imaging by enabling automated detection, risk stratification, and prognostic forecasting from mammography, ultrasound, and MRI data. Deep learning approaches have shown superior performance in identifying malignant lesions and stratifying patients compared to traditional radiological assessment, while radiomics and hybrid ML models have enhanced staging and treatment planning. Parallel to these developments, mathematical modeling of tumor growth, particularly using Gompertzian, logistic, and Bayesian population-based frameworks, provides mechanistic insights into tumor kinetics and predictive estimates of tumor age, size, and treatment response. Integrating AI-driven image analysis with tumor growth modeling offers a powerful interdisciplinary approach for early diagnosis, personalized treatment, and clinical decision support. This paper presents a comprehensive study that bridges these two paradigms, exploring case studies of AI-based detection systems alongside mathematical tumor-growth predictions. The novelty lies in demonstrating how synergistic use of computational intelligence and mechanistic modeling can improve diagnostic accuracy, reduce clinical workload, and enable personalized care pathways.