The project explores innovative methods for optimising key data processing steps in genetic analysis, with a particular focus on the initial stages of data transformation. A significant aspect of this research involves investigating how to improve the efficiency of counting and analysing genetic markers in large datasets. The goal is to develop processes that are both computationally efficient and biologically accurate, ensuring that results are reliable without incurring unnecessary computational costs.

Another core area of the research is the development of advanced artificial intelligence (AI) models that can detect genetic anomalies in real-time. These models aim to identify variations, such as mutations, that could be indicative of diseases or other genetic conditions. Leveraging machine learning techniques, the project focuses on fine-tuning algorithms that can efficiently and accurately classify and predict genetic anomalies with minimal latency.

In parallel with improving data processing speed, the project investigates new data compression techniques aimed at reducing memory and storage requirements for large genomic datasets. Efficient compression methods will allow for faster data access and analysis, making it feasible to work with the enormous volumes of data generated by modern sequencing technologies. Research in this area explores probabilistic models and techniques that offer reduced data footprints without sacrificing critical genetic information.

Real-time analysis is essential for applications requiring immediate results, such as clinical decision-making and early detection of genetic disorders. This project aims to achieve high-throughput real-time processing of genetic data, enabling the analysis of genomic sequences at rates that meet the needs of both research and clinical environments. The challenge here is balancing the need for speed with the requirement for biological accuracy, ensuring that results are both timely and reliable.