TL;DR
It is demonstrated that the accuracy of diagnosing autism can be improved by investigating the relationships between several behavioural traits using deep learning approach, and the graph-based machine learning models applied to behavioural reaction time features can provide clinically interpretable ASD classification.
The need to develop large, well‐balanced datasets, the application of explainable AI techniques, standardization and regulatory guidelines for facilitating the clinical translation of ASD detection systems are suggested.
The proposed AFSTGCN‐ALA model is evaluated using performance criteria such as compute time, F1‐score, accuracy and precision, and demonstrates the effectiveness and dependability of AFSTGCN‐ALA for early ASD identification.
A novel approach for distinguishing individuals with Autism Spectrum Disorder (ASD) using Intuitionistic Fuzzy Set (IFS) theory and Multi-Scale Enhanced Graph Convolutional Networks (MSE-GCNs), which represents a substantial improvement over existing models for ASD and potentially for other neurological disorders.
This work proposes a cross-attention-guided subject-adaptive graph network (CAS-GNN) model that integrates structural MRI and resting-state functional connectivity data, effectively fusing complementary multimodal information and provides a promising tool for interpretable and robust ASD diagnosis, accelerating biomarker discovery and development.
Motivation Graph Neural Networks (GNNs) have gained increasing interest in the biomedical domain, as the integration of prior knowledge and deep neural networks has the potential to enhance insights into molecular processes and disease mechanisms. However, a comprehensive and systematic assessment of model architectures, data modalities, graph structures, and their performance for graph signal classification in the biomedical domain is yet to be performed. In order to close this gap, we conducted a benchmarking study on multiple GNNs on a Protein-Protein Interaction (PPI) network for Kidney Renal Clear Cell Carcinoma and Breast cancer subtype prediction, performing an in-depth investigation of architectures, incorporating skip connections and various data modalities. Results While none of the GNNs outperforms the structure-agnostic Multi-Layer Perceptron baseline, all of them can handle bimodal data (gene methylation and expression) and offer the ability to gain explainability based on PPIs. We offer practical guidelines for applying GNNs to graph signal processing tasks specifically for cancer classification. Depending on the underlying dataset and PPI structure employed, models on different data modalities outperform others. Overall, we suggest using ChebNet, which tends to outperform the Graph Convolutional Network and the Graph Attention Network in cancer subtype prediction. We recommend using GNN architectures that employ a simple flattening readout layer, as they provide better classification performance and faster training time than those with global average pooling. Additionally, we tested residual connections, but they had only an insignificant impact on classification performance. Availability and implementation Code and data available at https://github.com/HauschildLab/GNN4PPI. Contact julia.schirmacher@med.uni-goettingen.de, anne-christin.hauschild@uni-gießen.de Supplementary information Supplementary data attached.
Study of the efficacy of GNN layers in a slew of regression contexts from rank ordering, error minimization and insight extraction shows that deep convolutional GNNs, particularly GEN, are more effective at these tasks than attention-based GNNs, while other classical, theoretically-inspired GNNs remain competitive and efficient.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
