Neural Population Decoding and Imbalanced Multi-Omic Datasets for Cancer Subtype Diagnosis

Charles Kent, Leila Bagheriye, Johan Kwisthout

2024

Abstract

Recent strides in the field of neural computation has seen the adoption of Winner-Take-All (WTA) circuits to facilitate the unification of hierarchical Bayesian inference and spiking neural networks as a neurobiologically plausible model of information processing. Current research commonly validates the performance of these networks via classification tasks, particularly of the MNIST dataset. However, researchers have not yet reached consensus about how best to translate the stochastic responses from these networks into discrete decisions, a process known as population decoding. Despite being an often underexamined part of SNNs, in this work we show that population decoding has a significant impact on the classification performance of WTA networks. For this purpose, we apply a WTA network to the problem of cancer subtype diagnosis from multi-omic data, using datasets from The Cancer Genome Atlas (TCGA). In doing so we utilise a novel implementation of gene similarity networks, a feature encoding technique based on Kohoen’s self-organising map algorithm. We further show that the impact of selecting certain population decoding methods is amplified when facing imbalanced datasets.

Paper Citation

in Harvard Style

Kent C., Bagheriye L. and Kwisthout J. (2024). Neural Population Decoding and Imbalanced Multi-Omic Datasets for Cancer Subtype Diagnosis. 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 391-403. DOI: 10.5220/0012454200003657

in Bibtex Style

@conference{bioinformatics24,
author={Charles Kent and Leila Bagheriye and Johan Kwisthout},
title={Neural Population Decoding and Imbalanced Multi-Omic Datasets for Cancer Subtype Diagnosis},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2024},
pages={391-403},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012454200003657},
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 - Neural Population Decoding and Imbalanced Multi-Omic Datasets for Cancer Subtype Diagnosis
SN - 978-989-758-688-0
AU - Kent C.
AU - Bagheriye L.
AU - Kwisthout J.
PY - 2024
SP - 391
EP - 403
DO - 10.5220/0012454200003657
PB - SciTePress