Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer Subtype Diagnosis
This work addresses a specific bottleneck in neural computation for cancer diagnosis, but it is incremental as it focuses on improving decoding methods within an existing framework.
The paper tackled the problem of translating stochastic responses from Winner Take All (WTA) spiking neural networks into discrete decisions for cancer subtype diagnosis, showing that population decoding significantly impacts classification performance, especially on imbalanced multi-omic datasets from TCGA.
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 Kohoens self organising map algorithm. We further show that the impact of selecting certain population decoding methods is amplified when facing imbalanced datasets.