Adjustive Linear Regression and Its Application to the Inverse QSAR

Jianshen Zhu, Kazuya Haraguchi, Hiroshi Nagamochi, Tatsuya Akutsu

2022

Abstract

In this paper, we propose a new machine learning method, called adjustive linear regression, which can be regarded as an ANN on an architecture with an input layer and an output layer of a single node, wherein an error function is minimized by choosing not only weights of the arcs but also an activation function at each node in the two layers simultaneously. Under some conditions, such a minimization can be formulated as a linear program (LP) and a prediction function with adjustive linear regression is obtained as an optimal solution to the LP. We apply the new machine learning method to a framework of inferring a chemical compound with a desired property. From the results of our computational experiments, we observe that a prediction function constructed by adjustive linear regression for some chemical properties drastically outperforms that by Lasso linear regression.

Paper Citation

in Harvard Style

Zhu J., Haraguchi K., Nagamochi H. and Akutsu T. (2022). Adjustive Linear Regression and Its Application to the Inverse QSAR. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-552-4, SciTePress, pages 144-151. DOI: 10.5220/0010853700003123

in Bibtex Style

@conference{bioinformatics22,
author={Jianshen Zhu and Kazuya Haraguchi and Hiroshi Nagamochi and Tatsuya Akutsu},
title={Adjustive Linear Regression and Its Application to the Inverse QSAR},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS},
year={2022},
pages={144-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010853700003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS
TI - Adjustive Linear Regression and Its Application to the Inverse QSAR
SN - 978-989-758-552-4
AU - Zhu J.
AU - Haraguchi K.
AU - Nagamochi H.
AU - Akutsu T.
PY - 2022
SP - 144
EP - 151
DO - 10.5220/0010853700003123
PB - SciTePress