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

arXiv: https://arxiv.org/abs/2206.05311 · HTML: https://mlsanity.com/paper/2206.05311/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 78 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation](https://mlsanity.com/paper/2106.06963/) (2021)
- [Text Generation from Knowledge Graphs with Graph Transformers](https://mlsanity.com/paper/1904.02342/) (2019)
- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)](https://mlsanity.com/paper/1808.04256/) (2018)
- [Graph Transformer for Graph-to-Sequence Learning](https://mlsanity.com/paper/1911.07470/) (2019)
- [Contrastive Attention for Automatic Chest X-ray Report Generation](https://mlsanity.com/paper/2106.06965/) (2021)
- [Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2105.07059/) (2021)
- [Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation](https://mlsanity.com/paper/2111.04318/) (2021)
- [Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations](https://mlsanity.com/paper/1905.06139/) (2019)
- [Towards Data Distillation for End-to-end Spoken Conversational Question Answering](https://mlsanity.com/paper/2010.08923/) (2020)
- [Knowledge Distillation for Improved Accuracy in Spoken Question Answering](https://mlsanity.com/paper/2010.11067/) (2020)
- [Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation](https://mlsanity.com/paper/2009.02831/) (2020)
- [Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery](https://mlsanity.com/paper/2002.06053/) (2020)
- [Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising](https://mlsanity.com/paper/1805.00587/) (2018)
- [Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension](https://mlsanity.com/paper/2012.10877/) (2020)
- [Rethinking Skip Connection with Layer Normalization in Transformers and ResNets](https://mlsanity.com/paper/2105.07205/) (2021)
- [simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions](https://mlsanity.com/paper/1808.08732/) (2018)
- [Exploring and Distilling Cross-Modal Information for Image Captioning](https://mlsanity.com/paper/2002.12585/) (2020)

## Built on by (most cited first, 5 of 5)

- [EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System](https://mlsanity.com/paper/2206.13441/) (2022)
- [Customizing General-Purpose Foundation Models for Medical Report Generation](https://mlsanity.com/paper/2306.05642/) (2023)
- [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024)
- [The End of Reward Engineering: How LLMs Are Redefining Multi-Agent Coordination](https://mlsanity.com/paper/2601.08237/) (2026)
- [MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks](https://mlsanity.com/paper/2210.07488/) (2022)

## Abstract (arXiv)

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.