A systematic review of graph neural network in healthcare-based applications: Recent advances, trends, and future directions
Graph neural network (GNN) is a formidable deep learning framework that enables the
analysis and modeling of intricate relationships present in data structured as graphs. In …
analysis and modeling of intricate relationships present in data structured as graphs. In …
Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability
F Abdullakutty, Y Akbari, S Al-Maadeed… - arXiv preprint arXiv …, 2024 - arxiv.org
It is imperative that breast cancer is detected precisely and timely to improve patient
outcomes. Diagnostic methodologies have traditionally relied on unimodal approaches; …
outcomes. Diagnostic methodologies have traditionally relied on unimodal approaches; …
[HTML][HTML] Addressing imbalance in graph datasets: Introducing gate-gnn with graph ensemble weight attention and transfer learning for enhanced node classification
AJ Fofanah, D Chen, L Wen, S Zhang - Expert Systems with Applications, 2024 - Elsevier
Significant challenges arise when Graph Neural Networks (GNNs) try to deal with uneven
data. Specifically in signed and weighted graph structures. This makes classification tasks …
data. Specifically in signed and weighted graph structures. This makes classification tasks …
Diagnosis of breast cancer molecular subtypes using machine learning models on unimodal and multimodal datasets
Breast cancer is a significant global health concern, with millions of cases and deaths each
year. Accurate diagnosis is critical for timely treatment and medication. Machine learning …
year. Accurate diagnosis is critical for timely treatment and medication. Machine learning …
Handling Over-Smoothing and Over-Squashing in Graph Convolution With Maximization Operation
Recent years have witnessed the great success of the applications of graph convolutional
networks (GCNs) in various scenarios. However, due to the challenging over-smoothing and …
networks (GCNs) in various scenarios. However, due to the challenging over-smoothing and …
Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning
W Ma, M Li, Z Chu, H Chen - Sensors, 2024 - mdpi.com
Biosensors play a crucial role in detecting cancer signals by orchestrating a series of
intricate biological and physical transduction processes. Among various cancers, breast …
intricate biological and physical transduction processes. Among various cancers, breast …
[HTML][HTML] Optimal gene therapy network: Enhancing cancer classification through advanced AI-driven gene expression analysis
TR Nethala, BK Sahoo, P Srinivasulu - e-Prime-Advances in Electrical …, 2024 - Elsevier
Gene therapy is an advanced medical approach that aims to find solutions for various
cancers by identifying optimal gene expressions. In this context, computer-aided detection of …
cancers by identifying optimal gene expressions. In this context, computer-aided detection of …
Transformer-based deep learning integrates multi-omic data with cancer pathways
Multi-omic data analysis incorporating machine learning has the potential to significantly
improve cancer diagnosis and prognosis. Traditional machine learning methods are usually …
improve cancer diagnosis and prognosis. Traditional machine learning methods are usually …
Contrastive Learning in Single-cell Multiomics Clustering
Recent advancements in single-cell multiomics sequencing technology present new
opportunities for researchers. However, the integrative analysis of the multiomics data poses …
opportunities for researchers. However, the integrative analysis of the multiomics data poses …
scGEMOC, A Graph Embedded Contrastive Learning Single-cell Multiomics Clustering Model
Recent advancements in single-cell multiomics sequencing create new research
opportunities but also pose challenges, particularly in cell clustering. One major challenge is …
opportunities but also pose challenges, particularly in cell clustering. One major challenge is …