Gene interaction graphs aim to capture various relationships between genes and can  be used to create more biologically-intuitive models for machine learning. There are many such graphs available which can differ in the number of genes and edges covered . In this work, we attempt to evaluate the biases provided by those graphs through utilizing them for ' Single Gene Inference' (SGI) which serves as, what we believe is, a proxy for more relevant prediction tasks. The SGI task assesses how well a gene's neighbors in a particular graph can 'explain' the gene itself in comparison to the baseline of using all the genes in the dataset. We evaluate seven major gene interaction graphs created by different research groups on two distinct datasets, TCGA and GTEx .  We find that some graphs perform on par with the unbiased baseline for most genes with a significantly smaller feature set .
Gene interaction graphs aim to capture various relationships between genes and can represent decades of biology research. When trying to make predictions from genomic data, those graphs could be used to overcome the curse of dimensionality by making machine learning models sparser and more consistent with biological common knowledge . In this work, we focus on assessing how well those graphs capture dependencies seen in gene expression data to evaluate the adequacy of the prior knowledge provided by those graphs . We propose a condition graphs should satisfy to provide good prior knowledge and test it using ` Single Gene Inference' tasks . We also compare with randomly generated graphs, aiming to measure the true benefit of using biologically relevant graphs in this context, and validate our findings with five clinical tasks. We find some graphs capture relevant dependencies for most genes while being very sparse. Our analysis with random graphs finds that dependencies can be captured almost as well at random which suggests that, in terms of gene expression levels, the relevant information about the state of the cell is spread across many genes .
A
represent decades of biology research. When trying to make predictions from genomic data, those graphs could
R
create more biologically-intuitive models for machine learning. There are many such graphs available which can differ in the number of genes and edges covered
overcome the curse of dimensionality by making machine learning models sparser and more consistent with biological common knowledge
R
attempt
focus on assessing how well those graphs capture dependencies seen in gene expression data
R
biases
adequacy of the prior knowledge
R
through utilizing them for '
. We propose a condition graphs should satisfy to provide good prior knowledge and test it using `
R
(SGI) which serves as, what we believe is, a proxy for more relevant prediction tasks. The SGI task assesses how well a gene's neighbors in a particular graph can 'explain' the gene itself in comparison to the baseline of using all the genes in the dataset. We evaluate seven major gene interaction graphs created by different research groups on two distinct datasets, TCGA and GTEx
tasks
A
We also compare with randomly generated graphs, aiming to measure the true benefit of using biologically relevant graphs in this context, and validate our findings with five clinical tasks.
R
that some graphs perform on par with the unbiased baseline
some graphs capture relevant dependencies
R
with a significantly smaller feature set
while being very sparse. Our analysis with random graphs finds that dependencies can be captured almost as well at random which suggests that, in terms of gene expression levels, the relevant information about the state of the cell is spread across many genes