# DeepGO: Predicting protein functions from sequence and interactions using a deep ontology-aware classifier

arXiv: https://arxiv.org/abs/1705.05919 · HTML: https://mlsanity.com/paper/1705.05919/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2017.

## Built on by (most cited first, 20 of 20)

- [Deep Learning with Logical Constraints](https://mlsanity.com/paper/2205.00523/) (2022)
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- [CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models](https://mlsanity.com/paper/2505.22869/) (2025)
- [Clustering for Protein Representation Learning](https://mlsanity.com/paper/2404.00254/) (2024)
- [Insights Into the Inner Workings of Transformer Models for Protein Function Prediction](https://mlsanity.com/paper/2309.03631/) (2023)
- [Informed deep hierarchical classification: a non-standard analysis inspired approach](https://mlsanity.com/paper/2409.16956/) (2024)
- [DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction](https://mlsanity.com/paper/2307.13004/) (2023)
- [An Integrated Deep Learning and Dynamic Programming Method for Predicting Tumor Suppressor Genes, Oncogenes, and Fusion from PDB Structures](https://mlsanity.com/paper/2105.08100/) (2021)
- [Leveraging Sequence Embedding and Convolutional Neural Network for Protein Function Prediction](https://mlsanity.com/paper/2112.00344/) (2021)
- [An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction](https://mlsanity.com/paper/2112.02810/) (2021)
- [Transformer Neural Networks Attending to Both Sequence and Structure for Protein Prediction Tasks](https://mlsanity.com/paper/2206.11057/) (2022)
- [Isoform Function Prediction Using a Deep Neural Network](https://mlsanity.com/paper/2208.03325/) (2022)
- [A Review of Deep Learning Techniques for Protein Function Prediction](https://mlsanity.com/paper/2211.09705/) (2022)
- [VALUED -- Vision and Logical Understanding Evaluation Dataset](https://mlsanity.com/paper/2311.12610/) (2023)
- [ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation](https://mlsanity.com/paper/2402.07955/) (2024)
- [ProteinRPN: Towards Accurate Protein Function Prediction with Graph-Based Region Proposals](https://mlsanity.com/paper/2409.00610/) (2024)
- [Transformers trained on proteins can learn to attend to Euclidean distance](https://mlsanity.com/paper/2502.01533/) (2025)
- [AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization](https://mlsanity.com/paper/2506.07035/) (2025)
- [A Novel Framework for Multi-Modal Protein Representation Learning](https://mlsanity.com/paper/2510.23273/) (2025)
- [Semantics-Aware Caching for Concept Learning](https://mlsanity.com/paper/2603.06506/) (2026)

## Abstract (arXiv)

A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40,000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, with significant improvement for predicting cellular locations.