The gene function prediction challenge: large language models and knowledge graphs to the rescue

Rohan Shawn Sunil, Shan Chun Lim, Manoj Itharajula, Marek Mutwil
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Abstract

Elucidating gene function is one of the ultimate goals of plant science. Despite this, only ~15% of all genes in the model plant Arabidopsis thaliana have comprehensively experimentally verified functions. While bioinformatical gene function prediction approaches can guide biologists in their experimental efforts, neither the performance of the gene function prediction methods nor the number of experimental characterisation of genes has increased dramatically in recent years. In this review, we will discuss the status quo and the trajectory of gene function elucidation and outline the recent advances in gene function prediction approaches. We will then discuss how recent artificial intelligence advances in large language models and knowledge graphs can be leveraged to accelerate gene function predictions and keep us updated with scientific literature.
基因功能预测挑战:大型语言模型和知识图谱的拯救
阐明基因功能是植物科学的终极目标之一。尽管如此,在模式植物拟南芥(Arabidopsis thalian)中,只有约 15% 的基因具有经过实验验证的全面功能。虽然生物信息学的基因功能预测方法可以指导生物学家的实验工作,但近年来基因功能预测方法的性能和基因实验表征的数量都没有显著提高。在这篇综述中,我们将讨论基因功能阐释的现状和发展轨迹,并概述基因功能预测方法的最新进展。然后,我们将讨论如何利用人工智能在大型语言模型和知识图谱方面的最新进展来加速基因功能预测,并使我们能及时了解科学文献的最新进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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