Finding Novel Diagnostic Gene Patterns Based on Interesting Non-redundant Contrast Sequence Rules

Diagnostic genes refer to the genes closely related to a specific disease phenotype, the powers of which to distinguish between different classes are often high. Most methods to discovering the powerful diagnostic genes are either singleton discriminability-based or combination discriminability-based. However, both ignore the abundant interactions among genes, which widely exist in the real world. In this paper, we tackle the problem from a new point of view and make the following contributions: (1) we propose an EWave model, which profitably exploits the ordered expressions among genes based on the defined equivalent dimension group sequences taking into account the "noise" universal in the real data, (2) we devise a novel sequence rule, namely interesting non-redundant contrast sequence rule, which is able to capture the difference between different phenotypes in a high accuracy using as few as possible genes, (3) we present an efficient algorithm called NRMINER to find such rules. Unlike the conventional column enumeration and the more recent row enumeration, it performs a novel template-driven enumeration by making use of the special characteristic of micro array data modeled by EWave. Extensive experiments conducted on various synthetic and real datasets show that: (1) NRMINER is significantly faster than the competing algorithm by up to about one order of magnitude, (2) it provides a higher accuracy using fewer genes. Many diagnostic genes discovered by NRMINER are proved biologically related to some disease.