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Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification
2015
Briefings in Bioinformatics
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
doi:10.1093/bib/bbv044
pmid:26141830
pmcid:PMC4870394
fatcat:rdvyk7eyurc2tk7lp6otlyq6ne