Using prior knowledge from cellular pathways and molecular networks for
diagnostic specimen classification
#MMPMID26141830
Glaab E
Brief Bioinform
2016[May]; 17
(3
): 440-52
PMID26141830
show ga
For many complex diseases, an earlier and more reliable diagnosis is considered a
key prerequisite for developing more effective therapies to prevent or delay
disease progression. Classical statistical learning approaches for specimen
classification using omics data, however, often cannot provide diagnostic models
with sufficient accuracy and robustness for heterogeneous diseases like cancers
or neurodegenerative disorders. In recent years, new approaches for building
multivariate biomarker models on omics data have been proposed, which exploit
prior biological knowledge from molecular networks and cellular pathways to
address these limitations. This survey provides an overview of these recent
developments and compares pathway- and network-based specimen classification
approaches in terms of their utility for improving model robustness, accuracy and
biological interpretability. Different routes to translate omics-based
multifactorial biomarker models into clinical diagnostic tests are discussed, and
a previous study is presented as example.
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