Rough set soft computing cancer classification and network: one stone, two birds

Abstract

Gene expression profiling provides tremendous information to help unravel the complexity of cancer. The selection of the most informative genes from huge noise for cancer classification has taken centre stage, along with predicting the function of such identified genes and the construction of direct gene regulatory networks at different system levels with a tuneable parameter. A new study by Wang and Gotoh described a novel Variable Precision Rough Sets-rooted robust soft computing method to successfully address these problems and has yielded some new insights. The significance of this progress and its perspectives will be discussed in this article.

Keywords: cancer; classification; gene expression profiling; network; rough sets; soft computing; α depended degree.

Figure 1.

Scheme of the “free lunch” toolkit for cancer classification at the network level and beyond. Arrow: executed Dash arrow: being executed “Free lunch” kit codes: the programming codes for cancer classification, hub gene identification and inference of gene regulatory network under GNU GPL.