Translational Robustness of Neural Networks Trained for Transcription Factor Binding Site Classification

Gergely Pap, István Megyeri

2022

Abstract

Classifying DNA sequences based on their protein binding profiles using Deep Learning has enjoyed considerable success in recent years. Although these models can recognize binding sites at high accuracy, their underlying behaviour is unknown. Meanwhile, adversarial attacks against deep learning models have revealed serious issues in the fields of image- and natural language processing related to their black box nature. Analysing the robustness of Transcription Factor Binding Site classifiers urges us to develop adversarial attacks for them. In this work, we introduce shifting as an adversarial data augmentation so that it quantifies the translational robustness. Our results show that despite its simplicity our attack can significantly affect performance. We evaluate two architectures using two data sets with three shifting strategies and train robust models with adversarial data augmentation.

Paper Citation

in Harvard Style

Pap G. and Megyeri I. (2022). Translational Robustness of Neural Networks Trained for Transcription Factor Binding Site Classification. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 39-45. DOI: 10.5220/0010769100003116

in Bibtex Style

@conference{icaart22,
author={Gergely Pap and István Megyeri},
title={Translational Robustness of Neural Networks Trained for Transcription Factor Binding Site Classification},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={39-45},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010769100003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Translational Robustness of Neural Networks Trained for Transcription Factor Binding Site Classification
SN - 978-989-758-547-0
AU - Pap G.
AU - Megyeri I.
PY - 2022
SP - 39
EP - 45
DO - 10.5220/0010769100003116