Exploring Machine Learning in Lung Cancer: Predictive Modelling, Gene Associations, and Challenges
Keywords:
Lung cancer, prediction, gene association, machine learning, high-throughput genomic data, multi-omics data, support vector machines, random forests, deep understanding, network-based methodologies, predictive modeling, biomarker identificationAbstract
Lung cancer is a disease with a high mortality rate and widespread occurrence. Therefore, developing accurate prediction methods and practical gene association analyses is crucial. The utilization of high-throughput genomic data to reveal significant genetic factors has seen an increase in the application of machine-learning techniques. This document presents a thorough examination of machine learning methodologies that are presently utilized to forecast lung cancer and scrutinize gene correlations. The analysis examines different data types, such as gene expression profiles, genomic variants, and clinical data. The primary focus is on integrating multi-omics data for a more comprehensive understanding. Our study comprehensively examines a variety of machine learning algorithms, including traditional methods such as support vector machines and random forests, advanced deep learning architectures, and network-based methodologies. The following discourse explores the pragmatic utilization of the methods above in predictive modeling, biomarker identification, and drug discovery routes. The article addresses common obstacles in the field, such as interpretability and validation, and proposes potential avenues for future research, such as incorporating multi-omics data and implementing personalized medicine. This survey provides a detailed analysis of the recent advancements in machine learning techniques for lung cancer research. It aims to establish a strong basis for future improvements in diagnosis, prognosis, and treatment strategies.
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