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

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. 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. Multi-omics data integration in cancer diagnosis Alternatively, some research focuses on the timedependent refers to the integration of information from various relationship of spiking neurons, for biological "omics" e.g., genomics, transcriptomics, instance by weighting neuron responses more highly metabolomics, to provide a more comprehensive based on how quickly they fire (Grün & Rotter, 2010; understanding of the molecular landscape of cancer. In order to extract information from SNNs, we neurobiologically inspired method of information examine the spikes generated by a population of processing which aim to solve tasks using plausible neurons in response to a stimulus.