Gene Prioritization

Gene prioritization aims to identify genes most relevant to a specific biological process or disease from a vast pool of candidates, streamlining research and reducing experimental costs. Current research heavily utilizes machine learning, employing diverse approaches such as positive-unlabeled learning, various neural network architectures (including graph neural networks), and robust statistical methods to handle class imbalances and high-dimensionality inherent in gene expression data. These advancements improve the accuracy and efficiency of identifying key genes, impacting areas like disease diagnosis, drug target identification, and understanding complex biological mechanisms.