AdaPPI: identification of novel protein functional modules via adaptive graph convolution networks in a protein–protein interaction network
Identifying unknown protein functional modules, such as protein complexes and biological
pathways, from protein–protein interaction (PPI) networks, provides biologists with an …
pathways, from protein–protein interaction (PPI) networks, provides biologists with an …
PathFinder: a novel graph transformer model to infer multi-cell intra-and inter-cellular signaling pathways and communications
Recently, large-scale scRNA-seq datasets have been generated to understand the complex
signaling mechanisms within the microenvironment of Alzheimer's Disease (AD), which are …
signaling mechanisms within the microenvironment of Alzheimer's Disease (AD), which are …
Spatiotemporal constrained RNA–protein heterogeneous network for protein complex identification
Z Li, S Wang, H Cui, X Liu, Y Zhang - Briefings in Bioinformatics, 2024 - academic.oup.com
The identification of protein complexes from protein interaction networks is crucial in the
understanding of protein function, cellular processes and disease mechanisms. Existing …
understanding of protein function, cellular processes and disease mechanisms. Existing …
Graph Node Classification to Predict Autism Risk in Genes
D Bandara, K Riccardi - Genes, 2024 - mdpi.com
This study explores the genetic risk associations with autism spectrum disorder (ASD) using
graph neural networks (GNNs), leveraging the Sfari dataset and protein interaction network …
graph neural networks (GNNs), leveraging the Sfari dataset and protein interaction network …
Autism Risk Classification using Graph Neural Networks Applied to Gene Interaction Data
K Riccard, D Bandara - 2023 Congress in Computer Science …, 2023 - ieeexplore.ieee.org
We use a gene interaction network to predict which genes are associated with Autism
Spectrum Disorder (ASD), thus allowing for earlier detection of ASD. ASD is a disorder that …
Spectrum Disorder (ASD), thus allowing for earlier detection of ASD. ASD is a disorder that …
Predicting Gene Relations with a Graph Transformer Network Integrating DNA, Protein, and Descriptive Data
Gene relation prediction is crucial for understanding cancer pathways and develo**
targeted treatments. This study proposes a novel Graph Transformer Network to predict …
targeted treatments. This study proposes a novel Graph Transformer Network to predict …
7 Exploring autism risk: a deep dive into graph neural networks and gene interaction data
D Bandara, K Riccardi - Big Data, Data Mining and Data Science …, 2024 - degruyter.com
Autism spectrum disorder (ASD) has many genetic connections that can be represented in
genetic association networks. These networks can be converted in graph data structure and …
genetic association networks. These networks can be converted in graph data structure and …
Multi-omics data integration via novel interpretable k-hop graph attention network for signaling network inference
With the advent of sequencing technology, large-scale multi-omics data have been
generated to understand the diversity and heterogeneity of genetic targets and associated …
generated to understand the diversity and heterogeneity of genetic targets and associated …