# Predicting O-GlcNAcylation Sites in Mammalian Proteins with Transformers and RNNs Trained with a New Loss Function

arXiv: https://arxiv.org/abs/2402.17131 · HTML: https://mlsanity.com/paper/2402.17131/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Temperature Schedules for Self-Supervised Contrastive Methods on Long-Tail Data](https://mlsanity.com/paper/2303.13664/) (2023)
- [Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization](https://mlsanity.com/paper/1905.11528/) (2019)
- [sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification](https://mlsanity.com/paper/2108.10566/) (2021)
- [Matthews Correlation Coefficient Loss for Deep Convolutional Networks: Application to Skin Lesion Segmentation](https://mlsanity.com/paper/2010.13454/) (2020)
- [A surrogate loss function for optimization of Fβ score in binary classification with imbalanced data](https://mlsanity.com/paper/2104.01459/) (2021)
- [Improving image classifiers for small datasets by learning rate adaptations](https://mlsanity.com/paper/1903.10726/) (2019)
- [A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering Models](https://mlsanity.com/paper/1708.00584/) (2017)

## Built on by (most cited first, 2 of 2)

- [Harmonic Loss Trains Interpretable AI Models](https://mlsanity.com/paper/2502.01628/) (2025)
- [Improving Performance in Classification Tasks with LCEN and the Weighted Focal Differentiable MCC Loss](https://mlsanity.com/paper/2604.21252/) (2026)

## Abstract (arXiv)

O-GlcNAcylation, a subtype of glycosylation, has the potential to be an important target for therapeutics, but methods to reliably predict O-GlcNAcylation sites had not been available until 2023; a 2021 review correctly noted that published models were insufficient and failed to generalize. Moreover, many are no longer usable. In 2023, a considerably better recurrent neural network (RNN) model was published. This article creates improved models by using a new loss function, which we call the weighted focal differentiable MCC. RNN models trained with this new loss display superior performance to models trained using the weighted cross-entropy loss; this new function can also be used to fine-tune trained models. An RNN trained with this loss achieves state-of-the-art performance in O-GlcNAcylation site prediction with an F1 score of 38.88% and an MCC of 38.20% on an independent test set from the largest dataset available.