Interpretable graph convolutional network enables triple negative ...

We develop an interpretable graph convolutional network (GCN) trained by IMC data with 30 cell markers from 238 patient breast cancer samples.

The network enables triple negative breast cancer (TNBC) classification from other clinical types, including HR+HER2+, HR+HER- and HR-HER+. More importantly, ...

Explaining decisions of graph convolutional neural networks: patient ...

Presenting interpretable patient-specific subnetworks to clinicians and researchers enables better interpretability ... High motility of triple- ...

CGMega: explainable graph neural network framework with ... - Nature

Transfer learning enables predictions in network ... The phosphatase PPM1A inhibits triple negative breast cancer growth by blocking cell cycle ...

[PDF] Interpretable Graph Convolutional Network Of Multi-Modality ...

An interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain ...

Explaining graph convolutional network predictions for clinicians ...

Table 3. Influence of each data group on classification results. 3.6 Explanation method for individual node classification. Interpretability is critical as it ...

An interpretable graph representation learning model for accurate ...

In this work, a drug aqueous solubility model is proposed, which adopts the 3-layer topology adaptive graph convolutional network (TAGCN) stacked sequentially, ...

Simplified, interpretable graph convolutional neural networks for ...

A richer analysis of our gCNN confusion matrix categories that allows one to access negative and confounding information will likely be the ...

Explaining graph convolutional network predictions for clinicians ...

Towards multi-modal causability with graph neural networks enabling information fusion for explainable AI. ... “Interpretable graph convolutional ...

AttentionSiteDTI: an interpretable graph-based model for drug-target ...

Graph convolutional neural network (GCNN) and graph attention network (GAT) [3] are the two widely used GNN-based models in computer-aided ...

GNNExplainer: Generating Explanations for Graph Neural Networks

At a high level, we can group those interpretability methods for non-graph neural networks into two main families. 2. Page 3. ,. ,. , ...

LiGCN: Label-interpretable Graph Convolutional Networks for Multi ...

Besides, the model allows better interpretabil- ity for predicted labels as the token-label edges are exposed. We evaluate our method on four.

A graph-based interpretability method for deep neural networks

... [3]. In medical applications, due to the black-box properties of deep ... The results from GCNs enable us to evaluate the parameters and determine ...

Deep Learning with Graph Convolutional Networks: An Overview ...

The coordinated development of algorithms and computing power will enable future applications to enter a new era of intelligence. Graph data is ...

Pre-training Interpretable Graph Neural Networks - NIPS papers

Pre-training Interpretable Graph Neural Network (π-GNN3) to distill the universal interpretability of GNNs by pre-training over synthetic graphs with ground- ...

An interpretable graph convolutional neural network based fault ...

The method is developed by following three main steps: (1) selecting NC-GNN as a fault diagnosis model for building energy systems and proposing a graph ...

naganandy/graph-based-deep-learning-literature - GitHub

EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network ... Beyond Graph Convolutional Network: An Interpretable Regularizer-Centered Optimization ...

Graph neural networks for an accurate and interpretable prediction ...

... enables an accurate and interpretable prediction of the properties of polycrystalline materials ... 3: Network architecture of the GNN model.

Knowledge-primed neural networks enable biologically ...

To enable interpretability, we exploit three modifications to ... It requires three inputs: (i) a neural network graph (KPNN or ANN) ...

Interpretable and Convergent Graph Neural Network Layers at Scale

The method enables training on very large graphs and achieves state ... On interpretability: it seems 3/4 reviewers (me, ik1J, PP1g) ...