Incorporating prior knowledge of predictors into penalized classifiers with multiple penalty terms

Bioinformatics
Feng Tai, Wei Pan

Abstract

In the context of sample (e.g. tumor) classifications with microarray gene expression data, many methods have been proposed. However, almost all the methods ignore existing biological knowledge and treat all the genes equally a priori. On the other hand, because some genes have been identified by previous studies to have biological functions or to be involved in pathways related to the outcome (e.g. cancer), incorporating this type of prior knowledge into a classifier can potentially improve both the predictive performance and interpretability of the resulting model. We propose a simple and general framework to incorporate such prior knowledge into building a penalized classifier. As two concrete examples, we apply the idea to two penalized classifiers, nearest shrunken centroids (also called PAM) and penalized partial least squares (PPLS). Instead of treating all the genes equally a priori as in standard penalized methods, we group the genes according to their functional associations based on existing biological knowledge or data, and adopt group-specific penalty terms and penalization parameters. Simulated and real data examples demonstrate that, if prior knowledge on gene grouping is indeed informative, our new methods perfo...Continue Reading

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Related Concepts

Biochemical Pathway
Gene Expression Regulation, Neoplastic
Gene Expression
Rietveld Refinement
Regression Analysis
Two-Parameter Models
Mammary Neoplasms, Human
Cdna Microarrays
Malignant Neoplasms
Computational Molecular Biology

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