Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge

Julius Voigt, Sascha Saralajew, Marika Kaden, Katrin Bohnsack, Katrin Bohnsack, Lynn Reuss, Thomas Villmann

2024

Abstract

We propose a biologically-informed shallow neural network as an alternative to the common knowledge-integrating deep neural network architecture used in bio-medical classification learning. In particular, we focus on the Generalized Matrix Learning Vector Quantization (GMLVQ) model as a robust and interpretable shallow neural classifier based on class-dependent prototype learning and accompanying matrix adaptation for suitable data mapping. To incorporate the biological knowledge, we adjust the matrix structure in GMLVQ according to the pathway knowledge for the given problem. During model training both the mapping matrix and the class prototypes are optimized. Since GMLVQ is fully interpretable by design, the interpretation of the model is straightforward, taking explicit account of pathway knowledge. Furthermore, the robustness of the model is guaranteed by the implicit separation margin optimization realized by means of the stochastic gradient descent learning. We demonstrate the performance and the interpretability of the shallow network by reconsideration of a cancer research dataset, which was already investigated using a biologically-informed deep neural network.

Paper Citation

in Harvard Style

Voigt J., Saralajew S., Kaden M., Bohnsack K., Reuss L. and Villmann T. (2024). Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-688-0, SciTePress, pages 357-367. DOI: 10.5220/0012420700003657

in Bibtex Style

@conference{bioinformatics24,
author={Julius Voigt and Sascha Saralajew and Marika Kaden and Katrin Bohnsack and Lynn Reuss and Thomas Villmann},
title={Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2024},
pages={357-367},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012420700003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge
SN - 978-989-758-688-0
AU - Voigt J.
AU - Saralajew S.
AU - Kaden M.
AU - Bohnsack K.
AU - Reuss L.
AU - Villmann T.
PY - 2024
SP - 357
EP - 367
DO - 10.5220/0012420700003657
PB - SciTePress