DNA Sequence Classification

DNA sequence classification aims to categorize DNA sequences based on their characteristics, enabling applications in diverse fields like genomics and forensics. Current research focuses on improving the accuracy and efficiency of classification using various machine learning approaches, including deep learning models like convolutional neural networks and transformer-based architectures, as well as more computationally efficient methods like compressor-based techniques and processing-in-memory architectures. Addressing challenges such as adversarial attacks, the need for large datasets, and the interpretability of complex models remains a key focus, with ongoing efforts to develop explainable AI methods and handle non-independent and non-identically distributed data. These advancements are crucial for accelerating genomic research, improving diagnostic tools, and enabling personalized medicine.