# Improved K-mer Based Prediction of Protein-Protein Interactions With Chaos Game Representation, Deep Learning and Reduced Representation Bias

arXiv: https://arxiv.org/abs/2310.14764 · HTML: https://mlsanity.com/paper/2310.14764/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 23 Oct 2023.

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

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- [Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection](https://mlsanity.com/paper/1708.01642/) (2017)
- [Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks](https://mlsanity.com/paper/1807.10225/) (2018)
- [Effective Use of Synthetic Data for Urban Scene Semantic Segmentation](https://mlsanity.com/paper/1807.06132/) (2018)
- [Semi-supervised dry herbage mass estimation using automatic data and synthetic images](https://mlsanity.com/paper/2110.13719/) (2021)
- [Scalable learning for bridging the species gap in image-based plant phenotyping](https://mlsanity.com/paper/2003.10757/) (2020)

## Abstract (arXiv)

Protein-protein interactions drive many biological processes, including the detection of phytopathogens by plants' R-Proteins and cell surface receptors. Many machine learning studies have attempted to predict protein-protein interactions but performance is highly dependent on training data; models have been shown to accurately predict interactions when the proteins involved are included in the training data, but achieve consistently poorer results when applied to previously unseen proteins. In addition, models that are trained using proteins that take part in multiple interactions can suffer from representation bias, where predictions are driven not by learned biological features but by learning of the structure of the interaction dataset. We present a method for extracting unique pairs from an interaction dataset, generating non-redundant paired data for unbiased machine learning. After applying the method to datasets containing _Arabidopsis thaliana_ and pathogen effector interations, we developed a convolutional neural network model capable of learning and predicting interactions from Chaos Game Representations of proteins' coding genes.