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Found 3 papers, 2 papers with code
Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer Subtype Diagnosis
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.
Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data
This paper proposes a novel approach to identify cancer subtypes through the integration of multi-omics data for clustering.
Bayesian Integration of Information Using Top-Down Modulated WTA Networks
The results show that WTA circuits are capable of integrating the probabilistic information represented by other WTA networks, and that top down processes can improve a WTA network's inference and learning performance.
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