Predictive Analysis of Cancer Presence Using Gene Expression Profiling
Abstract
Gene expression profiling has emerged as a powerful approach in identifying biomarkers for cancer diagnosis and prognosis. This study explores the relationship between the expression levels of two specific genes and the presence of cancer, utilizing a dataset containing 3000 samples. Through data visualization and supervised learning, we aim to develop a classification model that can predict cancer presence with high accuracy. Our findings suggest a strong correlation between gene expression levels and cancer diagnosis, underscoring the potential of genetic data in early detection efforts.
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Introduction
The early detection of cancer significantly improves treatment outcomes and survival rates. With the advent of high-throughput sequencing technologies, gene expression data has become invaluable in oncology research. This paper investigates how the expression levels of two genes correlate with cancer presence, using machine learning to build predictive models.
Conclusion
Gene expression data from just two genes can provide strong predictive power for cancer diagnosis. This has practical implications for developing simple and cost-effective screening tools. Future work could expand the feature space or apply deep learning for further performance gains.