K Mer

K-mers, short subsequences of a longer sequence (e.g., DNA, protein, or chemical structures), are increasingly used to represent complex biological and chemical data for machine learning applications. Current research focuses on leveraging k-mers within various model architectures, including transformer networks, graph neural networks, and convolutional neural networks, to improve tasks such as genomic selection, molecular fingerprinting, and protein-protein interaction prediction. This approach offers advantages in handling sequence data, enabling more accurate and efficient analyses across diverse fields, from crop breeding and drug discovery to pathogen detection and taxonomic classification. The resulting improvements in predictive accuracy and computational efficiency are driving significant advancements in these areas.