Tootfinder

Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
Taeho Jo, Eun Hye Lee, Alzheimer's Disease Sequencing Project
https://arxiv.org/abs/2506.00662

Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
INTRODUCTION: Alzheimer's disease (AD) is genetically complex, complicating robust classification from genomic data. METHODS: We developed a transformer-based ensemble model (TrUE-Net) using Monte Carlo Dropout for uncertainty estimation in AD classification from whole-genome sequencing (WGS). We combined a transformer that preserves single-nucleotide polymorphism (SNP) sequence structure with a concurrent random forest using flattened genotypes. An uncertainty threshold separated samples into …

The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs
B. Paroli, F. Borghi, M. A. C. Potenza, P. Milani
https://arxiv.org/abs/2506.19642

The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs
Threshold logic gates (TLGs) have been proposed as artificial counterparts of biological neurons with classification capabilities based on a linear predictor function combining a set of weights with the feature vector. The linearity of TLGs limits their classification capabilities requiring the use of networks for the accomplishment of complex tasks. A generalization of the TLG model called receptron, characterized by input-dependent weight functions allows for a significant enhancement of clas…