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This project, titled “Deep-Noncode: A Deep Learning Framework to Predict the Regulatory Effects of Noncoding Genetic Variants in Cancer,” focuses on solving a very real problem in modern biology and medicine how to quickly and accurately understand genetic mutations from massive genomic datasets. Today, technologies like Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) generate enormous amounts of data. A single human genome can contain millions of genetic variants. While this data is extremely valuable for diagnosing diseases, especially cancer, analyzing it is a major challenge. Traditional tools such as SIFT, PolyPhen, and CADD help predict whether a mutation is harmful, but they are usually slow and work one after another. This means that processing a full genome can take several hours or even days, which is not ideal in clinical situations where doctors need results quickly. This project addresses that problem by building a smart, high-speed system that combines big data technologies and artificial intelligence. Instead of analyzing genetic data sequentially, the system uses a distributed computing approach with Hadoop and MapReduce. This allows the data to be split into smaller parts and processed simultaneously across multiple computers. As a result, the system becomes much faster and more efficient, capable of handling very large datasets within a short time. Another important feature of the project is the use of ensemble learning. Instead of relying on just one prediction tool, the system combines the results from multiple tools like SIFT, PolyPhen, and CADD. These predictions are then merged using a voting mechanism to make a final decision. This improves accuracy because each tool has its strengths, and combining them reduces errors. What makes this project even more advanced is its focus on noncoding genetic variants. Unlike coding regions that directly produce proteins, noncoding regions regulate how genes are expressed. Mutations in these regions can still have serious effects, especially in diseases like cancer, but they are harder to study. To address this, the project includes a deep learning model that can analyze both DNA sequences and epigenomic data to predict how these noncoding variants affect gene regulation. The system also integrates real-time data from biological databases such as ClinVar and ENCODE, ensuring that predictions are based on the most up-to-date scientific knowledge. Additionally, it is designed with reliability and security in mind, including fault tolerance and data privacy measures, which are essential for handling sensitive patient information. Overall, this project is a powerful combination of biology, computer science, and artificial intelligence. It aims to make genomic analysis faster, more accurate, and more practical for real-world clinical use. By doing so, it supports the growing field of precision medicine, where treatments can be tailored to an individual’s genetic makeup. In simple terms, this project helps turn complex genetic data into meaningful insights that can improve diagnosis, guide treatment, and ultimately save lives.