Abstract
Gene function prediction is a crucial step in understanding the biological roles of genes and their involvement in diseases. Computational tools have become invaluable for predicting gene function, particularly for genes with unknown or poorly characterized functions. This article reviews various computational methods, including sequence-based approaches, machine learning algorithms, and network-based methods, used to predict gene function. We also discuss the challenges in gene function prediction, such as the incomplete annotation of genomes and the complexity of gene-environment interactions.
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