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2014 ; 30
(12
): i60-68
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Inductive matrix completion for predicting gene-disease associations
#MMPMID24932006
Natarajan N
; Dhillon IS
Bioinformatics
2014[Jun]; 30
(12
): i60-68
PMID24932006
show ga
MOTIVATION: Most existing methods for predicting causal disease genes rely on
specific type of evidence, and are therefore limited in terms of applicability.
More often than not, the type of evidence available for diseases varies-for
example, we may know linked genes, keywords associated with the disease obtained
by mining text, or co-occurrence of disease symptoms in patients. Similarly, the
type of evidence available for genes varies-for example, specific microarray
probes convey information only for certain sets of genes. In this article, we
apply a novel matrix-completion method called Inductive Matrix Completion to the
problem of predicting gene-disease associations; it combines multiple types of
evidence (features) for diseases and genes to learn latent factors that explain
the observed gene-disease associations. We construct features from different
biological sources such as microarray expression data and disease-related textual
data. A crucial advantage of the method is that it is inductive; it can be
applied to diseases not seen at training time, unlike traditional
matrix-completion approaches and network-based inference methods that are
transductive. RESULTS: Comparison with state-of-the-art methods on diseases from
the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed
approach is substantially better-it has close to one-in-four chance of recovering
a true association in the top 100 predictions, compared to the recently proposed
Catapult method (second best) that has <15% chance. We demonstrate that the
inductive method is particularly effective for a query disease with no previously
known gene associations, and for predicting novel genes, i.e. genes that are
previously not linked to diseases. Thus the method is capable of predicting novel
genes even for well-characterized diseases. We also validate the novelty of
predictions by evaluating the method on recently reported OMIM associations and
on associations recently reported in the literature. AVAILABILITY: Source code
and datasets can be downloaded from
http://bigdata.ices.utexas.edu/project/gene-disease.
free
free
free
