Level 2: Interaction Networks
SWAN 35
SWAN incorporates prior knowledge network into the cutoff selection
process for correlation networks. This is a hybrid inference/extraction
algorithm that redefines the inference task as defining a cutoff
threshold such that the agreement with prior information is maximized.
SWAN works by first constructing a correlation network from the data.
The network is then filtered to remove edges that are not statistically
significant. The remaining edges are then ranked according to their
strength. Prior interaction networks can be easily integrated with
inferred correlation networks. SWAN then selects a cutoff for the
network based on prior knowledge. To calculate the correlation, SWAN
uses shrinkage partial correlation based on the GeneNet algorithm –
although this approach can be generalized to any correlation metric. The
overlap is measured using Fisher’s exact test p-value, which indicates
the agreement between the calculated correlation network and prior
knowledge. The optimal cutoff is defined as the point where the overlap
is maximal.
SWAN was tested on pan-cancer data of 26 cancer types extracted from The
Cancer Genome Atlas (TCGA). The network was able to identify enriched
genes (OG) in the elevated pathways and suppressed genes (TSG) within
suppressed pathways with a p-value < 0.05. This result was
compared with the Gene Set Enrichment Analysis (GSEA) and revealed that
SWAN outperformed GSEA. To check if SWAN can study race-specific CNA
patterns, ovarian cancer samples from an African American population
were collected, and non-Hispanic white patients were used as control.
SWAN identified that the cytokine pathway was elevated in the former
population which can be mapped to the overall poor prognosis in these
patients. Furthermore, SWAN was also able to figure out the effect of
the knockdown of metallothionein 2A which led to an increase in
formation of ɤH2AX foci.
GLRP 36
Graph Layer-wise Relevance Propagation (GLRP) is a novel method that
extends the Layer-wise Relevance Propagation (LRP) technique to Graph
Convolutional Neural Networks (Graph-CNN). LRP is an existing technique
that explains the decisions made by deep learning models. The primary
goal of GLRP is to explain the classification results of various omics
data and molecular networks which could facilitate the decision-making
processes in personalized medicine.
This is a unimodal, hybrid phenotype prediction and
explanation/extraction algorithm that aims to ground predicted graphs to
known protein-protein interaction networks. GLRP interprets the
classification output by leveraging the molecular network and also
produces patient-specific subnetworks that can be used to explain
clinical outcomes and therapeutic vulnerabilities.
GLRP was trained on gene expression datasets of breast cancer and human
umbilical vein endothelial cells (HUVECs). Their predictive performance
was evaluated using the 10-fold cross-validation method. In the breast
cancer study, GLRP was used to classify patients into metastatic and
non-metastatic groups. The results were compared with the classification
performance of random forest and glmgraph models as well as weighted
gene co-expression network analysis. GLRP outperformed the other models,
and the developed patient-specific subnetworks identified meaningful
features in breast cancer samples.