Chromosome Abnormality

Chromosome abnormalities, encompassing both numerical and structural variations, are a significant focus of genetic research due to their association with numerous diseases. Current research emphasizes developing automated methods for detecting and classifying these abnormalities, particularly focusing on advanced image analysis techniques like deep learning models (e.g., variational autoencoders, YOLOv4, and graph neural networks) to improve the accuracy and efficiency of karyotyping. These advancements aim to streamline the diagnostic process, enabling faster and more precise identification of chromosomal disorders and contributing to improved patient care and personalized medicine. Furthermore, research is exploring novel computational representations of chromosomes to enhance the performance of algorithms used in genetic analysis and optimization problems.