This paper proposes a hybrid ensemble classifier with denoising autoencoder (ECDAE) framework to address reliability and robustness challenges in cooperative spectrum sensing (CSS) for cognitive radio networks (CRNs). The proposed framework first emp...
BACKGROUND: The development of spatial transcriptomics (ST) has enabled biologists to measure transcriptome data on an entire tissue and retain spatial information. It gives us the opportunity to fully understand the tissue microenvironment and ident...
Automated arrhythmia detection from electrocardiogram (ECG) signals is crucial and important for the early treatment of cardiac disease (CD). In this investigation, eight machine-learning models have been developed to identify improved ECG arrhythmia...
Metabolism, a series of complex and orderly chemical reactions within a biological organism, has a significant impact on sustaining life activities. Disease development is often linked to alterations in the types or levels of metabolites; however, tr...
The practical adoption of deep learning in high-stakes forensic applications, such as dental age estimation, is often limited by the 'black box' nature of the models. This study introduces a framework designed to enhance both performance and transpar...
Traditional histopathological analysis of cells and tissue relies on morphological features from stained biopsy samples, which fail to leverage the wealth of chemical information about the underlying pathological states. Raman spectroscopy, a form of...
Neural network models for outcome prediction play a pivotal role in neurological disease research, particularly for baseline risk assessment. Schizophrenia, a complex and relatively rare neuropsychiatric disorder, presents significant diagnostic chal...
Understanding the intricate relationship between brain structure and function is a cornerstone challenge in neuroscience, critical for deciphering the mechanisms that underlie healthy and pathological brain function. In this work, we present a compre...
Journal of computer-aided molecular design
Nov 12, 2025
Deep generative models may detect novel compounds with favourable features, exhibiting chemical design potential. Traditional single-stage variational autoencoders (VAEs) lack validity, uniqueness, and biologically meaningful distribution alignment. ...
Small nucleolar RNAs (snoRNAs), a class of non-coding RNAs broadly distributed in eukaryotes, are emerging as pivotal regulators in the field of epigenomics. In addition to guiding 2'-O-methylation and pseudouridylation modifications at specific rRNA...
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