Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification.

Abstract:

: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.

journal_name

Brief Bioinform

authors

Glaab E

doi

10.1093/bib/bbv044

subject

Has Abstract

pub_date

2016-05-01 00:00:00

pages

440-52

issue

3

eissn

1467-5463

issn

1477-4054

pii

bbv044

journal_volume

17

pub_type

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