Year: 2005
Type: article
Abstract: A majority of diseases are caused by a combination of factors, for example, composite genetic mutation profiles have been found in many cases to predict a deleterious outcome. There are several statistical techniques that have been used to analyze these types of biological data. This article implements a general strategy which uses data adaptive regression methods to build a specific pathway model, thus predicting a disease outcome by a combination of biological factors and assesses the significance of this model, or pathway, by using a permutation based null distribution. We also provide several simulation comparisons with other techniques. In addition, this method is applied in several different ways to an HIV-1 dataset in order to assess the potential biological pathways in the data.
Institution: University of California, Berkeley
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W14944000