Detecting 2D NMR Signals Using Mask RCNN

Hadeel Alghamdi, Alexei Lisitsa, Igor Barsukov, Rudi Grosman

2023

Abstract

Picking peaks in two-dimensional Nuclear Magnetic Resonance (NMR) spectra has been a critical research problem and a very time-consuming important step in further analyses of NMR biological molecular systems. Here, we implemented machine learning approach for peak detection and segmentation using machine learning framework Mask R-CNN.The model was trained on a large number of synthetic spectra of known configurations, and we show that our model demonstrates promising results up to 0.93 accuracy. We implemented uniform scaling on the data matrix during training to further improve detection to achieve 10.17% FPs and 1.7% FNs rate. We show the utility of Mask R-CNN on NMR spectra where the data range plays an important role in peak detection.

Paper Citation

in Harvard Style

Alghamdi H., Lisitsa A., Barsukov I. and Grosman R. (2023). Detecting 2D NMR Signals Using Mask RCNN. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 940-947. DOI: 10.5220/0011804700003393

in Bibtex Style

@conference{icaart23,
author={Hadeel Alghamdi and Alexei Lisitsa and Igor Barsukov and Rudi Grosman},
title={Detecting 2D NMR Signals Using Mask RCNN},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={940-947},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011804700003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Detecting 2D NMR Signals Using Mask RCNN
SN - 978-989-758-623-1
AU - Alghamdi H.
AU - Lisitsa A.
AU - Barsukov I.
AU - Grosman R.
PY - 2023
SP - 940
EP - 947
DO - 10.5220/0011804700003393