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Authors: Gergely Pap and István Megyeri

Affiliation: University of Szeged, Hungary

Keyword(s): Transcription Factor Binding Site, Convolutional Neural Networks, Adversarial Training, Sequence Motifs.

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.

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 39-45. DOI: 10.5220/0010769100003116

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Pap, G.
AU - Megyeri, I.
PY - 2022
SP - 39
EP - 45
DO - 10.5220/0010769100003116
PB - SciTePress

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