Graph-in-Graph Network for Automatic Gene Ontology Description Generation

TL;DR

This paper introduces a novel Graph-in-Graph neural network to automatically generate descriptive sentences for Gene Ontology terms, leveraging GO's structural information to improve biological understanding.

Contribution

The paper proposes a new Graph-in-Graph network architecture specifically designed for GO term description generation, enhancing existing sequence-to-sequence models with structural insights.

Findings

Significant performance improvements across multiple metrics

Up to 39.1% relative improvement in METEOR score

Effective leveraging of GO structural information

Abstract

Gene Ontology (GO) is the primary gene function knowledge base that enables computational tasks in biomedicine. The basic element of GO is a term, which includes a set of genes with the same function. Existing research efforts of GO mainly focus on predicting gene term associations. Other tasks, such as generating descriptions of new terms, are rarely pursued. In this paper, we propose a novel task: GO term description generation. This task aims to automatically generate a sentence that describes the function of a GO term belonging to one of the three categories, i.e., molecular function, biological process, and cellular component. To address this task, we propose a Graph-in-Graph network that can efficiently leverage the structural information of GO. The proposed network introduces a two-layer graph: the first layer is a graph of GO terms where each node is also a graph (gene graph). Such a Graph-in-Graph network can derive the biological functions of GO terms and generate proper descriptions. To validate the effectiveness of the proposed network, we build three large-scale benchmark datasets. By incorporating the proposed Graph-in-Graph network, the performances of seven different sequence-to-sequence models can be substantially boosted across all evaluation metrics, with up to 34.7%, 14.5%, and 39.1% relative improvements in BLEU, ROUGE-L, and METEOR, respectively.