Network-Based Detection of Autism Spectrum Disorder Using Sustainable and Non-invasive Salivary Biomarkers

• URL: http://arxiv.org/abs/2509.16126v1
• Date: Fri, 19 Sep 2025 16:24:48 GMT
• Title: Network-Based Detection of Autism Spectrum Disorder Using Sustainable and Non-invasive Salivary Biomarkers
• Authors: Janayna M. Fernandes, Robinson Sabino-Silva, Murillo G. Carneiro,
• Abstract summary: We developed GANet, a genetic algorithm-based network optimization framework leveraging PageRank and Degree for importance-based feature characterization.<n>It achieved superior performance compared to linear discriminant analysis, support vector machines, and deep learning models, reaching 0.78 accuracy, 0.61 sensitivity, 0.90 specificity, and a 0.74 harmonic mean PageRank.<n>These results demonstrate GANet's potential as a robust, bio-inspired, non-invasive tool for precise ASD detection and broader spectral-based health applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autism Spectrum Disorder (ASD) lacks reliable biological markers, delaying early diagnosis. Using 159 salivary samples analyzed by ATR-FTIR spectroscopy, we developed GANet, a genetic algorithm-based network optimization framework leveraging PageRank and Degree for importance-based feature characterization. GANet systematically optimizes network structure to extract meaningful patterns from high-dimensional spectral data. It achieved superior performance compared to linear discriminant analysis, support vector machines, and deep learning models, reaching 0.78 accuracy, 0.61 sensitivity, 0.90 specificity, and a 0.74 harmonic mean. These results demonstrate GANet's potential as a robust, bio-inspired, non-invasive tool for precise ASD detection and broader spectral-based health applications.

Related papers

• Spectral Gating Networks [65.9496901693099]
  We introduce Spectral Gating Networks (SGN) to introduce frequency-rich expressivity in feed-forward networks.<n>SGN augments a standard activation pathway with a compact spectral pathway and learnable gates that allow the model to start from a stable base behavior.<n>It consistently improves accuracy-efficiency trade-offs under comparable computational budgets.
  arXiv  Detail & Related papers  (2026-02-07T20:00:49Z)
• A Unified Matrix-Spectral Framework for Stability and Interpretability in Deep Learning [0.0]
  We develop a unified framework for analyzing stability and interpretability in deep neural networks.<n>We introduce a Global Matrix Stability Index that aggregates spectral information from Jacobians, parameter gradients, Neural Tangent Kernel operators, and loss Hessians into a single stability scale.
  arXiv  Detail & Related papers  (2026-02-01T10:18:37Z)
• DNA: Uncovering Universal Latent Forgery Knowledge [39.19379714306656]
  forgery detection capability is already encoded within pre-trained models.<n>DNA framework employs a coarse-to-fine excavation mechanism.<n>Hifi-Gen is a high-fidelity synthetic benchmark built upon the very latest models.
  arXiv  Detail & Related papers  (2026-01-30T03:48:30Z)
• Rapid Machine Learning-Driven Detection of Pesticides and Dyes Using Raman Spectroscopy [0.5002873541686897]
  pesticides and synthetic dyes pose critical threats to food safety, human health, and environmental sustainability.<n>Raman spectroscopy offers molecularly specific fingerprints but suffers from spectral noise, fluorescence background, and band overlap.<n>Here, we propose a deep learning framework based on ResNet-18 feature extraction to detect pesticides and dyes from Raman spectroscopy, called MLRaman.
  arXiv  Detail & Related papers  (2025-11-15T11:35:55Z)
• Enhancing ECG Classification Robustness with Lightweight Unsupervised Anomaly Detection Filters [39.9470953186283]
  Continuous electrocardiogram (ECG) monitoring via wearables offers significant potential for early cardiovascular disease (CVD) detection.<n> deploying deep learning models for automated analysis in resource-constrained environments faces reliability challenges due to Out-of-Distribution data.<n>This paper explores Unsupervised Anomaly Detection (UAD) as an independent, upstream filtering mechanism to improve robustness.
  arXiv  Detail & Related papers  (2025-10-30T13:54:37Z)
• Bridging Accuracy and Interpretability: Deep Learning with XAI for Breast Cancer Detection [0.0]
  We present an interpretable deep learning framework for the early detection of breast cancer using quantitative features extracted from digitized fine needle aspirate (FNA) images of breast masses.<n>Our deep neural network, using ReLU activations, the Adam visualizations, and a binary cross-entropy loss, delivers state-of-the-art classification performance.
  arXiv  Detail & Related papers  (2025-10-18T07:47:26Z)
• A Neural Network Approach to Multi-radionuclide TDCR Beta Spectroscopy [12.470638217209851]
  Liquid scintillation triple-to-doubly coincident ratio (TDCR) spectroscopy is widely adopted as a standard method for radionuclide quantification.<n>Here, we present an Artificial Intelligence framework that combines numerical spectral simulation and deep learning for standard-free automated analysis.
  arXiv  Detail & Related papers  (2025-09-03T08:40:02Z)
• SpectrumFM: Redefining Spectrum Cognition via Foundation Modeling [65.65474629224558]
  We propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition.<n>An innovative spectrum encoder that exploits the convolutional neural networks is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data.<n>Two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations.
  arXiv  Detail & Related papers  (2025-08-02T14:40:50Z)
• SDTN and TRN: Adaptive Spectral-Spatial Feature Extraction for Hyperspectral Image Classification [1.2871580250533408]
  Hyperspectral image classification plays a pivotal role in precision agriculture, providing accurate insights into crop health monitoring, disease detection, and soil analysis.<n>Traditional methods struggle with high-dimensional data, spectral-spatial redundancy, and the scarcity of labeled samples, often leading to suboptimal performance.<n>To address these challenges, we propose the Self-Adaptive- Regularized Network (SDTN), which combines tensor decomposition with regularization mechanisms to dynamically adjust tensor ranks.<n>This approach not only maintains high classification accuracy but also significantly reduces computational complexity, making the framework highly suitable for real-time deployment in resource-constrained environments.
  arXiv  Detail & Related papers  (2025-07-13T04:53:33Z)
• Optimized Spectral Fault Receptive Fields for Diagnosis-Informed Prognosis [8.719982934025415]
  Spectral Fault Receptive Fields (SFRFs) is a technique for degradation state assessment in bearing fault diagnosis and remaining useful life estimation.<n>SFRFs are designed as antagonistic spectral filters centered on characteristic fault frequencies.<n>A multi-objective evolutionary optimization strategy is employed to tune the receptive field parameters.
  arXiv  Detail & Related papers  (2025-06-14T07:12:56Z)
• Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics [40.044978986425676]
  The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accuracy.<n>We propose a novel unsupervised anomaly generation methodology that takes into account the engine physics model.<n>We propose Signature-Guided Data Augmentation (SGDA), an unsupervised framework that synthesizes physically plausible faults directly in the frequency domain of healthy current signals.
  arXiv  Detail & Related papers  (2025-06-10T03:36:16Z)
• SpectrumFM: A Foundation Model for Intelligent Spectrum Management [99.08036558911242]
  Existing intelligent spectrum management methods, typically based on small-scale models, suffer from notable limitations in recognition accuracy, convergence speed, and generalization.<n>This paper proposes a novel spectrum foundation model, termed SpectrumFM, establishing a new paradigm for spectrum management.<n>Experiments demonstrate that SpectrumFM achieves superior performance in terms of accuracy, robustness, adaptability, few-shot learning efficiency, and convergence speed.
  arXiv  Detail & Related papers  (2025-05-02T04:06:39Z)
• A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders [3.9517125314802306]
  We propose a self-supervised learning paradigm for Raman spectroscopy based on a Masked AutoEncoder, termed SMAE.<n> SMAE does not require any spectral annotations during pre-training. By randomly masking and then reconstructing the spectral information, the model learns essential spectral features.
  arXiv  Detail & Related papers  (2025-04-21T10:44:06Z)
• A Graph Based Raman Spectral Processing Technique for Exosome Classification [0.4749981032986242]
  "omics" approach is preferable to individual biomarkers.<n> Raman spectroscopy is effective for exosome analysis, but requires high sample concentrations and has limited sensitivity to lipids and proteins.<n>In this study, we leverage Neo4j graph databases to organize 3,045 Raman spectra of exosomes.
  arXiv  Detail & Related papers  (2025-04-21T09:19:41Z)
• Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data [36.92842246372894]
  Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN) is a deep learning framework that utilizes messenger-RNA, micro-RNA sequences, and DNA methylation samples.<n>By integrating multi-omics data with graph-based deep learning, our proposed approach demonstrates robust predictive performance and interpretability.
  arXiv  Detail & Related papers  (2025-03-29T02:14:05Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.