Neural Population Decoding and Imbalanced Multi- Omic Datasets For Cancer Subtype Diagnosis
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2023-12-01
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en
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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 significanct 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.
Cancer Diagnosis, Multi-Omics, Population Decoding, Spiking Neural Networks, Winner-Take-All,
Hierarchical Bayesian Network, Self-Organising Maps.
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Faculteit der Sociale Wetenschappen