# Protein Secondary Structure Prediction with Long Short Term Memory Networks

arXiv: https://arxiv.org/abs/1412.7828 · HTML: https://mlsanity.com/paper/1412.7828/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2014.

## Built on by (most cited first, 3 of 3)

- [Exploring evolution-aware & -free protein language models as protein function predictors](https://mlsanity.com/paper/2206.06583/) (2022)
- [Protein Language Models and Structure Prediction: Connection and Progression](https://mlsanity.com/paper/2211.16742/) (2022)
- [Protein Secondary Structure Prediction Using 3D Graphs and Relation-Aware Message Passing Transformers](https://mlsanity.com/paper/2511.13685/) (2025)

## Abstract (arXiv)

Prediction of protein secondary structure from the amino acid sequence is a classical bioinformatics problem. Common methods use feed forward neural networks or SVMs combined with a sliding window, as these models does not naturally handle sequential data. Recurrent neural networks are an generalization of the feed forward neural network that naturally handle sequential data. We use a bidirectional recurrent neural network with long short term memory cells for prediction of secondary structure and evaluate using the CB513 dataset. On the secondary structure 8-class problem we report better performance (0.674) than state of the art (0.664). Our model includes feed forward networks between the long short term memory cells, a path that can be further explored.