<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<ArticleSet>
<Article>
<Journal>
<PublisherName>International Academy of Ecology and Environmental Sciences</PublisherName>
<JournalTitle>Network Biology</JournalTitle>
<issn>2220-8879</issn>
<Volume>5</Volume>
<Issue>4</Issue>
<PubDate PubStatus="ppublish">
<Year>2015</Year>
<Month>12</Month>
<Day>1</Day>
</PubDate>
</Journal>
<ArticleTitle>A hierarchical method for finding interactions: Jointly using linear
 correlation and rank correlation analysis</ArticleTitle>
<Pages>137-145</Pages>
<Language>EN</Language>
<AuthorList>
<Author>WenJun Zhang</Author>
</AuthorList>
<ArticleList>
<ArticleId IdType="url">http://www.iaees.org/publications/journals/nb/articles/2015-5(4)/a-hierarchical-method-for-finding-interactions.pdf</ArticleId>>
</ArticleList>
<Abstract>
In the earlier studies, I pointed out that a network changed in a local domain can be approximated as a linear network, i.e., all between-node (or -taxon, -component, etc) changes in the local domain are treated as linear ones and Pearson linear correlation measure can be used. For a little wider domain, the quasi-linear measure, Spearman rank correlation can be used also. In present study, I jointly use Pearson linear correlation measure and Spearman rank correlation measure and their partial correlations to find interactions. First, I define some hierarchical principles for finding interactions. Reliability levels are then defined using set operations. The full algorithm and Matlab codes for finding interactions are given.
</Abstract>
</Article>
</ArticleSet>
